The Skeptics Guide to Emergency Medicine

Date: March 26, 2026 Dr. Rob Leeper Guest Skeptic: Dr. Robert Leeper is a trauma surgeon at the London Health Sciences Centre and an ATLS instructor who has helped train generations of physicians in trauma care. He has previously joined SGEM for: SGEM #200 – Bloodletting and Alexander Hamilton SGEM #256 – RLQ Pain and Appendectomy SGEM #345 – Non-operative Management of Appendicitis It’s SGEM Xtra time, where we go beyond a single paper and dive into broader topics that impact our daily practice. Now, some of you may remember that back in 2018, we did a Top 10 list for ATLS 10th Edition. Yes, we cranked it up to 10. ATLS 10th Edition: Top 10 Changes But today… We’re not stopping at 10. Because this SGEM episode goes to 11. If you don’t get that reference, go watch This Is Spinal Tap. It’s a mockumentary about a fictional rock band whose amplifiers go to 11 instead of 10. And when asked why they didn’t just make 10 louder, the guitarist replies: “These go to 11.” And that brings us to ATLS, now officially in its 11th edition. For those who don’t know the history of ATLS, here is the brief back story. ATLS was born out of tragedy. In 1976, orthopedic surgeon Dr. James Styner crashed his small plane in rural Nebraska. His wife died at the scene. He and his children survived but were severely injured. When they arrived at a small hospital, the trauma care they received was, by his account, disorganized and inadequate. Styner later said: “When I can provide better care in the field with limited resources than my children and I received at the primary care facility, there is something wrong with the system.” That moment led to the development of a structured approach to trauma, one that could be taught, replicated, and standardized. The first ATLS course was introduced by the American College of Surgeons (ACS) in 1980. It emphasized something radical at the time: a systematic, prioritized assessment of trauma patients, beginning with Airway, Breathing, Circulation, Disability, Exposure (ABCDE). In EM, our alphabet is A-B-CT, send them to the donut of truth. But back to the 1980s, the systematic ABCDE approach wasn’t about memorizing injuries. It was about preventing death from the first thing that kills. Over the decades, ATLS became one of the most widely adopted trauma education programs in the world. It has trained hundreds of thousands of clinicians in over 80 countries. And like any long-running franchise (Star Wars, Mission Impossible, Star Trek and Batman), each new edition tries to improve on the original. So today, instead of a Top 10 list as we did for ATLS 10, we’re going with: The 5 important changes in ATLS 11. Because sometimes less is more. Even if the amplifier goes to 11. Five Changes to the ATLS 11th Edition 1. xABCDE – Hemorrhage Now Comes Before Airway: The most noticeable clinical change in ATLS 11 is the addition of the “x” to ABCDE, making it xABCDE, with the “x” standing for exsanguinating hemorrhage. Massive external bleeding is now formally prioritized before airway management in select patients. While many trauma teams have already internalized the “bleeding kills first” principle, especially after a decade of military-to-civilian trauma translation, ATLS has now codified it. In practical terms, this reinforces early tourniquet use, direct pressure, and hemostatic adjuncts as first-line priorities when appropriate. It’s less of a revolution and more of an official acknowledgment that the trauma world has already turned the volume up on hemorrhage control. But formalizing it in the primary survey does matter, because what gets taught gets practiced. 2. Hemodynamic Optimization Before Intubation: Another subtle but important evolution in the 11th edition is the greater emphasis on resuscitating shock before proceeding with rapid sequence intubation (RSI). ATLS 11 highlights the risk of peri-intubation hypotension and arrest in unstable trauma patients, encouraging clinicians to correct hemodynamics before pushing paralytics. This aligns with growing emergency medicine literature around the dangers of precipitous airway management in the shocked patient. It’s a welcome shift toward physiologic thinking rather than purely procedural thinking. In other words, it reminds us that the airway isn’t just anatomy, it’s physiology. 3. Major Structural Reorganization and Systems Focus: The changes to ATLS 11 aren’t just clinical. This edition reorganizes the manual into three major sections: resuscitation, trauma systems/context, and specific injury patterns. More notably, it introduces full chapters on Trauma Systems, Injury Prevention, Trauma-Informed Care, and Communicating Serious News. This reflects a broader view of trauma care that extends beyond the primary survey. ATLS is no longer just about what happens in the first 15 minutes. It is also about the system in which those 15 minutes occur. For instructors, this may feel like an expansion into public health. Whether that’s evolution or mission creep may depend on your worldview. But it’s clear ATLS is trying to move from protocol to platform. 4. Dedicated Penetrating Trauma Chapter: Penetrating trauma now has its own standalone chapter in the 11th edition. This shows recognition that penetrating injury has unique management considerations compared to blunt trauma. The new edition emphasizes mechanism-driven evaluation, selective non-operative management, and updated surgical decision-making paradigms. For the USA trauma systems, this is particularly relevant given the epidemiology of violence-related injury (acute lead poisoning…Gun Shot Wounds [GSWs]). GSWs are the leading cause of death in the US for children aged 1 to 17 years. The key question, from an SGEM lens, is whether the content fully reflects contemporary evidence, especially regarding selective non-operative approaches. But structurally, this is a meaningful shift that gives penetrating trauma its own intellectual real estate. Dr. Andrew Worster 5. “Standardized Flexibility” – A Global Adaptation Philosophy: Perhaps the most philosophically important change in ATLS 11 is the formal adoption of “standardized flexibility.” The manual explicitly acknowledges global variability in trauma resources. Some places have CT availability, blood products, and access to specialist care, while others do not. ATLS now encourages adapting principles to the setting, rather than assuming Level I trauma center capabilities everywhere. This is a recognition that trauma education must be globally applicable. It moves ATLS from a rigid protocol toward a framework. It reminds me of the Evidence-Based Medicine (EBM) answer I learned from my mentor, Dr. Andrew Worster, “It all depends”. Traumas occur in a context (urban/rural/remote, academic/community, low-resource/high-resource, etc.). How ATLS is applied in your clinical situation will depend on many factors and requires flexibility. Other changes we wanted to mention: Head and spine combined into “disability.” Expanded section on geriatric trauma (now “Trauma in the Older Adult”) Enhanced team communication emphasis Hybrid learning and required pre-course videos Updated transfer mnemonic: S-xABCDE-BAR S: Situation (Who/Where/Why): Your name & role, location, patient demographics, mechanism of injury and reason for transfer.  xABCDE: Primary Survey Summary x – Exsanguinating Hemorrhage: Tourniquet? Pelvic binder? Massive Transfusion Protocol activated?Ongoing bleeding? A – Airway: Patent? Intubated? Endotracheal tube size? C-spine protected? B – Breathing O₂ saturation? Chest tube? Vent settings? Tension pneumothorax addressed? C – Circulation: Blood Pressure/Heart Rate? Intravenous/Intraosseous access? Blood products given?TXA given? D – Disability: Glasgow Coma Scale? Pupils? Lateralizing deficits? E – Exposure: Other injuries? Temperature? Hypothermia prevention? BAR: (B) Background: Past Medical History. Medications (Anticoagulants?). Allergies. Baseline function. (A) Assessment: Confirmed injuries. Working diagnosis. Clinical concerns. (R) Recommendations: Immediate needs? Operating Room? Intensive Care Unit? Imaging? Surgical team activation? SGEM Bottom Line: ATLS 11th Edition doesn’t radically reinvent trauma care. However, it formalizes hemorrhage-first thinking, expands systems-based trauma care, modernizes structure and teaching and recognizes global variation. The SGEM will be back next episode with a structured critical appraisal of a recent publication. Our goal is to shorten the knowledge translation (KT) window from over 10 years to less than 1 year by leveraging the power of social media. So, patients get the best care, based on the best evidence.  Remember to be skeptical of anything you learn, even if you heard it on the Skeptics’ Guide to Emergency Medicine.

Reference: Wong KH, et al. Improving Use of Oral Antihistamines in a Children’s Hospital. Pediatrics. Feb 2026; Date: March 15, 2026 Dr. Stephanie Kubala Guest Skeptic: Dr. Stephanie Kubala is an attending physician in the Division of Allergy and Immunology at Children’s Hospital of Philadelphia. She is double board-certified in both pediatrics and allergy and immunology. Case: A 5-year-old girl is brought in by her parents for an itchy rash. Her symptoms started last night. The parent reports an itchy, raised red rash on her trunk and extremities. She has not had any fever. She does not have any difficulty breathing, wheezing, vomiting, or diarrhea. On your exam, you note hives on her body but no lip or tongue swelling. Her lungs are clear to auscultation. She intermittently scratches at the rash. Her parents tell you, “We gave her a dose of diphenhydramine last night, and it may have helped a little, but it seems to have worn off. Can you help?” Background: In a lot of emergency departments, “hives = diphenhydramine” is practically muscle memory. It’s familiar, it’s been around forever, and families often expect it because it’s what they already have at home. As with many medical interventions, we must weigh potential harms against potential benefits. The problem is that diphenhydramine and other first-generation antihistamines like hydroxyzine come with a bunch of potential side effects, such as sedation, anticholinergic side effects, and unpredictable behavior changes in some kids. It doesn’t always last very long, which can lead to repeat dosing and frustrated families when symptoms come back a few hours later. On the other hand, second-generation antihistamines like cetirizine target the same H1 receptor for itch and urticaria but tend to be longer-acting and better tolerated, which is why many guidelines and expert groups prefer them for routine allergic symptoms. And there’s a bigger safety angle here, too: first-generation agents show up in dosing errors and misuse/overdose cases. The real issue isn’t whether second-generation antihistamines like cetirizine work. They do. We need to start asking why our systems still nudge clinicians toward the older first-generation antihistamines as a default. The issue is well-suited to a quality improvement (QI) study. Before we dive into the details of the study itself, let’s talk about some basics around QI. QI helps close the gap between best practice and day-to-day care. It starts with a clear, measurable aim (what you want to improve, by how much, by when). This is followed by a simple measurement plan: an outcome measure (the main result you’re trying to change), process measures (the steps that should drive that result), and balancing measures (what might worsen unintentionally). Teams then map the current workflow, identify barriers, and build a key driver diagram that links the aim to the handful of system levers most likely to move the needle. The work is tested and refined using Plan–Do–Study–Act (PDSA) cycles. [2] These are iterative rather than a single big rollout. Data is tracked over time with run/control charts to show whether changes are real and sustained. Clinical Question: Can a bundled QI approach meaningfully reduce first generation antihistamine use and increase cetirizine use among pediatric patients receiving oral antihistamines in the ED and inpatient settings? Reference: Wong KH, et al. Improving Use of Oral Antihistamines in a Children’s Hospital. Pediatrics. Feb 2026; Population: Patients 6 months to 21 years in the pediatric ED and inpatient units at a tertiary academic children’s hospital Excluded: Patients in NICU, PICU, or hematology-oncology units Intervention: There were 3 main drivers: education/awareness, cetirizine availability, and standardization through clinical pathways. Comparison: Pre-intervention baseline prescribing practices Outcome: Primary Outcomes: There are two primary outcomes: The proportion receiving oral FGA and the proportion receiving cetirizine Secondary Outcomes: PED revisits within 48 hours, median LOS, clinicians’ knowledge, frequency of clinical pathway use and monthly antihistamine cost. Type of Study: Quality improvement initiative Authors’ Conclusions: “Using the Model for Improvement, we reduced FGA use and increased cetirizine use in the PED and inpatient setting.” Quality Checklist for Ql Study (adapted from QI-MQCS): Do they clearly state the problem and why it mattered? Yes Do they explain why the intervention should improve the outcome? Yes Are the specific changes described in enough detail that another site could reproduce them? Unsure  Do they describe the setting the intervention took place (type of hospital/clinic, size, population)? Yes  Do they describe the approach to designing and introducing the program? Yes Is the evaluation approach explicit? Yes Do they describe what they are comparing against? Yes Are data sources clear and is the primary outcome operationally defined? Yes Is the timeline clear? Yes Do they measure whether the intervention was actually delivered/used as intended? Yes  Do they include patient health outcomes? No  Do they describe organizational barriers/facilitators that affect readiness? Yes Do they report who/what was eligible vs who/what actually participated? Yes Do they describe the maintenance and sustainability of their interventions over time? Yes Do they address whether the intervention could be replicated elsewhere? Yes Do they discuss limitations? Yes Funding of the Study. No funding for this study. No declared conflicts of interest. Results: The study included 1235 pediatric ED patients and 737 inpatients. They undertook a total of 5 PDSA cycles, including the ED and inpatient units. Key Results: FGA use decreased and cetirizine use increased after implementation of QI initiatives. The use of FGA decreased from 74% to 28% in the pediatric ED and 54% to 36% in the inpatient units. The use of cetirizine went from 31% to 75% in the pediatric ED and 54% to 74% un the inpatient units. The changes were sustained for 8.5 months in the pediatric ED and 9 months in the inpatient units. Secondary Outcomes Knowledge assessment improved (among 31 surveyed participants): Median 50% to 100%. Clinical pathway usage increased: Median 36 to 44 clinicians/month. Balancing measures: ED revisit within 48 hours and median inpatient LOS remained stable. Cost: Monthly median antihistamine costs increased (PED $53 to $177; inpatient $57 to $104), with discussion of unit cost drivers for cetirizine formulation. A crucial part of any QI process is the identification of key stakeholders. This study included representation from the pediatric ED, a pediatric resident (a great inclusion given that residents rotate through so many units in the hospital), allergy and immunology, and pharmacy. These stakeholders helped the group identify the key drivers that included education and awareness, availability of cetirizine, and standardization of preferred medication. Individuals from each group of stakeholders also acted as champions to help push the QI initiative. Uncontrolled Before-After Design QI studies do not necessarily need to include control groups. However, without a concurrent control group/unit, improvements can reflect background practice drift, staffing changes, guideline diffusion, seasonal case-mix shifts, or other QI initiatives rather than the intervention itself. are a classic threat in time-based comparisons. In addition, when clinicians are aware a practice is being measured (or receive peer-to-peer feedback), behavior can shift independent of the intrinsic effectiveness of the intervention. This is well described as the Hawthorne effect and the related sentinel effect [3]. Intervention Bundle Because they implemented multiple components (education, stocking, reminders, pathway updates, audit/feedback), the observed effect can’t be confidently attributed to any single change. This is a common challenge with complex interventions, where fidelity and mechanism can vary across units and time.  Education and Awareness Reliance on purely educational interventions for QI will likely only have a limited effect. For this study, the authors did a 30-minute lecture during a staff meeting and emailed the lecture materials to absent staff. They also put up flyers in work areas. This intervention in the first PDSA cycle did not result in a consistent reduction in first-generation antihistamine use. Based on feedback from a PDSA flyer, they also shortened the educational sessions for the inpatient implementation phase, recognizing that attention wanes after 15-20 minutes. Cetirizine Availability The group worked with the pharmacy in the hospital to ensure the cetirizine solution and tablets were available in the medication dispensing machines. They emailed the pediatric ED staff to let them know. It is important to recognize that some children may either be too young or unable to swallow tablets or pills. This seems like a basic step, but if we want people to start using something new or different, we should try to make it accessible to them. Standardizing Preferred Antihistamines Now that the alternative second-generation antihistamine is available, there’s another step this group took to help with adopting the change. They looked at the existing clinical pathways for anaphylaxis and penicillin allergy delabeling and changed the primary antihistamine recommendation from diphenhydramine to cetirizine. This is a nice way of making it easy for people to adopt the change. For those already accustomed to using the clinical pathway and order set, this does not really change workflow at all. Indications for Antihistamines

Date: March 11, 2026 Reference: RENOVATE Investigators and the BRICNet Authors; High-Flow Nasal Oxygen vs Noninvasive Ventilation in Patients With Acute Respiratory Failure: The RENOVATE Randomized Clinical Trial. JAMA March 2025 Guest Skeptic: Dr. Rory Spiegel is an emergency medicine and critical care physician known for his work in evidence-based medicine and critical care. He is widely recognized for translating emerging research into practical bedside insights through lectures, writing, and digital medical education. His work focuses on resuscitation science, airway management, and the critical appraisal of medical literature. I’m in Maui at the Centre for Continuing Medical Education Year in Review Course. CCME has been doing courses for almost 40 years. The courses take place at amazing locations in the US, including Maui, Hilton Head, Key West, and NYC. CCME recruits four outstanding educators to review ~260 articles from the past year. It’s a unique course because there are no PowerPoint slides to get in the way of the attendees and the speakers. Two faculty members summarize a few articles on a topic in ½ hour with direct interaction with the speakers. You come to this course…you are up to date on the latest EM literature.   Case: A 64-year-old woman with a history of COPD (GOLD stage III) and hypertension presents to the emergency department (ED) with worsening shortness of breath over the past 24 hours. She reports increased sputum production and wheezing. On arrival, she is tachypneic and speaking in short phrases. Her vital signs are heart rate 104 beats per minute, blood pressure 148/86 mm Hg, respiratory rate 30 breaths per minute, and SpO₂ 88% on 4 L nasal cannula. She is using accessory muscles and has diffuse expiratory wheezes on auscultation. An arterial blood gas reveals pH 7.29, PaCO₂ 58 mm Hg, and PaO₂ 62 mm Hg. Chest X-ray shows hyperinflation without focal consolidation. Background: Acute respiratory failure (ARF) is one of the most common serious respiratory problems managed in emergency medicine and critical care. For decades, noninvasive ventilation (NIV) has been a central part of therapy for selected patients. This is particularly true for those with COPD exacerbations and acute cardiogenic pulmonary edema. By delivering positive pressure, NIV reduces the work of breathing, improves oxygenation and ventilation. This intervention has been shown to reduce intubation rates and mortality in specific populations. However, NIV can be poorly tolerated, requires a tight mask seal and monitoring, and is resource-intensive [1-3]. These downsides can become more problematic in disease states that are not readily reversible over the first few hours. High-flow nasal oxygen (HFNO) has emerged over the past decade as an attractive potential alternative. By delivering heated, humidified oxygen at high flow rates, HFNO improves oxygenation, improves ventilator efficiency by reducing dead space, and is often better tolerated than mask-based ventilation. Its physiologic appeal and ease of use have led to widespread adoption, particularly during the COVID-19 pandemic. Yet enthusiasm has at times outpaced evidence, and important clinical questions remain:  Is HFNO equivalent/non-inferior to NIV in preventing intubation or death? How does it perform across different types of respiratory failure? And when should clinicians choose one over the other? Clinical Question: Is HFNO noninferior to NIV regarding the rates of endotracheal intubation or death at 7 days across five distinct patient groups with ARF? Reference: RENOVATE Investigators and the BRICNet Authors; High-Flow Nasal Oxygen vs Noninvasive Ventilation in Patients With Acute Respiratory Failure: The RENOVATE Randomized Clinical Trial. JAMA March 2025 Population: Hospitalized adults with ARF (hypoxemia plus respiratory effort or tachypnea) classified into 5 groups: Nonimmunocompromised with hypoxemia Immunocompromised with hypoxemia COPD exacerbation with respiratory acidosis Acute cardiogenic pulmonary edema (ACPE) Hypoxemic COVID-19 Exclusions: The main exclusion criteria were if there was an urgent need for endotracheal intubation, hemodynamic instability or contraindications to NIV. Intervention: High-flow nasal oxygen (HFNO) delivered continuously, titrated toward 60 L/min. Comparison: Noninvasive ventilation (NIV) delivered through a face mask. Outcome: Primary Outcome: Endotracheal intubation or death within 7 days. Secondary Outcomes: 28-day and 90-day mortality, mechanical ventilation-free days, and ICU-free days. Type of Study: Multicenter, adaptive, noninferiority randomized clinical trial using a Bayesian hierarchical model with dynamic borrowing across patient groups. Authors’ Conclusions: “Compared with NIV, HFNO met prespecified criteria for noninferiority for the primary outcome of endotracheal intubation or death within 7 days in 4 of the 5 patient groups with ARF. However, the small sample sizes in some patient groups and the sensitivity of the findings to the choice of analysis model suggests the need for further study in patients with COPD, immunocompromised patients, and patients with ACPE.” Quality Checklist for Randomized Clinical Trials: The study population included or focused on those in the emergency department. Yes The patients were adequately randomized. Yes  The randomization process was concealed. Yes  The patients were analyzed in the groups to which they were randomized. Yes The study patients were recruited consecutively (i.e. no selection bias). Yes The patients in both groups were similar with respect to prognostic factors. Unsure All participants (patients, clinicians, outcome assessors) were unaware of group allocation. No  All groups were treated equally except for the intervention. Unsure  Follow-up was complete (i.e. at least 80% for both groups). Yes  All patient-important outcomes were considered. Yes The treatment effect was large enough and precise enough to be clinically significant. Unsure  Funding: Supported by a grant from the Brazilian Ministry of Health. Fisher & Paykel Healthcare provided the high-flow nasal oxygen equipment and associated disposables. The trial coordinating center and sponsor were the Hcor Research Institute. “The funders/sponsors had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.” Financial conflicts of interest. Multiple authors reported multiple COIs, with one author receiving personal fees from Fisher & Paykel Healthcare. Results: RENOVATE randomized 1,800 hospitalized adults with ARF across 33 hospitals in Brazil, with 1,766 completing the trial. The mean age was 64 years, and 40% of participants were women. The population was primarily older adults with moderate to severe respiratory failure. The largest of the five pre-defined subgroups (almost half) consisted of patients with hypoxemic COVID-19. Key Result: HFNO was noninferior to NIV for the composite outcome of endotracheal intubation or death within 7 days in four out of five subgroups. Primary Outcome: Endotracheal intubation or death Overall, 39% HFNO vs 38% NIV Noninferiority: Met in four of the pre-specified groups (Nonimmunocompromised, COPD, ACPE, and COVID-19). Immunocompromised Group: Stopped for futility (57.1% HFNO vs 36.4% NIV). Secondary Outcomes: There were no statistically or clinically meaningful differences in 28-day mortality, 90-day mortality, mechanical ventilation-free days, or ICU-free days overall. However, subgroup-specific secondary outcome estimates were imprecise and should be interpreted cautiously. 1. Diverse Etiology: The authors enrolled all adult patients presenting with hypoxic respiratory failure to non-invasive support with either HFNO or NIV. Using such a broad enrollment criterion led them to enroll a wide variety of clinical etiologies in this trial. The advantages of such broad inclusion criteria mean that the results can be applied broadly to patients presenting with hypoxic respiratory failure. In this case, it is likely accurate to say that neither NIV nor HFNO is superior when treating an undifferentiating population of patients with hypoxic respiratory failure. A disadvantage is that the 5 subtypes of respiratory failure represent very different physiological causes of ARF, which may respond differently to different forms of respiratory support. There may, in fact, exist potential benefits for either NIV or HFNO in one or more of these specific subgroups that are obscured when looking at this greater population. The authors address this by performing subgroup analyses for each of the 5 predefined subgroups. Unfortunately, due to the small sample sizes in each subgroup, there were too few patients to confidently demonstrate that one form of non-invasive support is preferred over the other. Therefore, the broad inclusion criteria used by these authors make it very difficult to identify a potential benefit of NIV or HFNO in any one subtype of hypoxic respiratory failure. For example, among immunocompromised patients with hypoxemia, the primary outcome (endotracheal intubation and death within 7 days) occurred in 57.1% of patients in the HFNO group vs 36.4% of patients in the NIV group. Due to the small sample sizes (28 and 22 patients in the two groups, respectively), the 20.7% difference met criteria for futility, as the confidence intervals were too wide to demonstrate non-inferiority or superiority. Open Label 2. Lack of Masking: This was an open-label trial where both clinicians and patients knew which treatment was assigned.

Date: March 5, 2026 Today, we’re not in the studio. We’re not in Canada. We’re not even in North America. We are in Oxford. And not just Oxford, we are recording this SGEM Xtra in a pub. This will be the second-ever SGEM PUBcast. We need to travel back in time to 2012 for the first PUBcast. That happened when I came to Oxford for a mini-fellowship at the Centre for Evidence-Based Medicine (CEBM) on how to teach evidence-based medicine (SGEM#6). I had no idea that experience would change my professional career and open so many doors for me around the world. In that early SGEM episode, we did a structured critical appraisal of a 2011 BMJ article by Subramanian et al. called: Orthopaedic surgeons: as strong as an ox and almost twice as clever? Multicentre prospective comparative study. That trial examined the dominant grip strength of male orthopedic surgeons compared with male anesthesiologists. No surprise, they found orthopedic surgeons had significantly greater grip strength. However, they also compared the two specialties using an intelligence score and found that orthopedists scored significantly higher than anesthetists. The SGEM bottom line was that the stereotypical image of male orthopedic surgeons as strong but stupid is unjustified in comparison with their male anesthetist counterparts. Well, the SGEM has grown over the last 14 years, with greater than 85,000 subscribers, has been translated into four other languages, and has more than 600 episodes. Tonight, we are back in Oxford at the historic St. Aldate’s Tavern. We are surrounded by centuries of scholarship, skepticism, and possibly a few pints of beer. Joining me to co-host this SGEM Xtra PUBcast is the wonderful Melanie Golob. She is a DPhil candidate in Evidence-Based Health Care here at Oxford. Melanie is also the HTA Program & FFS Operations Manager in the US. Melanie Golob has been a shining star of the DPhil Program for Evidence-Based Health Care and a real ambassador of Evidence-Based Medicine (EBM). Some of us who are older might say you are the Julie McKoy of the DPhil program. She makes everyone feel welcome and appreciated. Questions for Melanie Golob Listen to the SGEM Xtra podcast on iTunes or Spotify to hear Melanie's responses. Question#1: Why Oxford? What drew you here for your DPhil? Was it the Centre for Evidence-Based Medicine (CEBM) specifically? Is there something “statistically significant” about Oxford’s approach to EBM? Does being in a place with this much academic history change how you think? Responds Question#2: What is Your Research About? What problem are you trying to solve with a Living Evidence Synthesis (LES)? Why does “living” evidence matter? Are we ready for AI-assisted living evidence? Question#3: Advice for Future Oxford Students What advice would you give someone interested in doing a DPhil in Evidence-Based Health Care? What makes someone a good candidate? What’s the hardest and most rewarding part? Questions for DPhil Candidates Layal and Taylor Who are you (name and where are you from), and what brought you to Oxford? What is your area of research? What is the most challenging thing about being at Oxford, and the best thing? That concludes the second SGEM PUBcast. We will be back next episode, trying to cut the knowledge translation window from over 10 years to less than 1 year with the power of social media. Remember to be skeptical of anything you learn, even if you heard it on the Skeptics’ Guide to Emergency Medicine. Note: Other people mentioned on the PUBcast Ross Drain- 4th Year Medical Student at Keble College, University of Oxford Juliana Louw - 5th Year Medicine Student at University of Oxford and President of Oxford Lifestyle Medicine Society Carl Heneghen - Professor of Evidence-Based Medicine, University of Oxford Liam Barrett - Emergency Medicine Trainee pursuing a DPhil in Medical Sciences at the University of Oxford Nicholas De Vito - Postdoctoral researcher at the Bennett Institute for Applied Data Science Layal Bou Harfouch- Drug Policy Analyst at the Reason Foundation, DPhil Candidate at the University of Oxford and Founder of Omniwomyn Taylor Hirschberg - CEO Scientist, AI Healthcare Researcher, Pulitzer and GLAAD nominated, Documentary Film Maker and DPhil candidate at the University of Oxford.

Date: February 26, 2026 Guest Skeptic: Terry O’Reilly is the host of the long-running and popular podcast Under the Influence. He is also an acclaimed storyteller and book writer. However, Terry is not just some radio host talking about marketing; he was an adman on the front lines, working in the trenches for 35 years in the advertising industry. I’ve been a listener of Under the Influence for a long time, and it’s helped me think about how we communicate with emergency clinicians and how we make ideas memorable without overselling them. I see many similarities with Terry. I’m not just some podcaster talking about emergency medicine. I’ve been working in the emergency department (ED), on the front line, for 31 years. I’m not an academic sitting in an Ivory tower opining on how to practice emergency medicine based on the literature. I worked 17 ED shifts in February. I’m walking the walk while I talk the talk. I think that brings a perspective and credibility to the SGEM, similar to the credibility of what Terry does on Under the Influence. Terry and I met in person with my wife, Barb, and 11-year old son, Ethan, around 2009. Terry was promoting his book The Age of Persuasion: How Marketing Ate Our Culture. We pulled Ethan out of school to go to Sarnia for a day and watch him give a talk. Terry even signed a copy of his book for Ethan. Our son was so inspired by the event and went on to pursue an academic career in Marketing. Ethan will be defending his PhD in Marketing from the Ivey School of Business this spring. Today, we are going to talk about Terry's latest book: Against the Grain: Defiant Giants Who Changed the World. It is a collection of stories about people who challenged the status quo and changed what the rest of us thought was possible. It reminded me of Apple's famous commercial, "Think Different." I made a parody video about rural physicians titled “Here’s to the Crazy Ones”. People may be wondering why this matters to emergency physicians. I think the “against the grain” ethos is common in emergency medicine. We have healthy skepticism and often challenge dogma, based on the evidence, when discussing management with other specialties. We also must be good at persuading patients, families, learners, consultants, and administrators that what we are doing is the right thing. Five Questions for Terry O'Reilly 1) What inspired you to write Against the Grain? Was there a single person/story that sparked the project? What’s your definition of defiant? Did you notice a pattern in how these defiant giants resisted the herd/groupthink? 2) What was one of the most surprising stories you uncovered while researching the book? What surprised you: the person’s personality, the risk they took, or how others reacted? Was there a moment in your researching a story where you thought, “No way this is true”, and then it was? 3) There are four medical stories in the book (Chapter 4). Most SGEMers probably know about Ignaz Philipp Semmelweis. Can you briefly tell us the story of Dr. Katalin Karikó  Katalin Karikó and Drew Weissman 2023 Nobel Prize in Medicine for their discoveries concerning nucleoside base modifications that enabled the development of effective mRNA vaccines against COVID-19. Do you think healthcare messaging has unique challenges compared with marketing products? In your view, what’s the difference between educating vs persuading in healthcare? We do need to be careful in science and medicine not to commit the Galileo Fallacy. This is when someone assert a is true or should be given more credibility because the person making the claim has been prosecuted or otherwise mocked. This fallacy originates from Galileo Galilei's famous persecution by the Roman Catholic Church for his defence of heliocentrism, when the commonly accepted belief at the time was an earth-centred universe. The truth is independent of whether the person is being mocked/persecuted, as with Semmelweis. What matters is the objective, verifiable evidence and logical arguments.  4) What has the feedback been like on the book tour so far? Which types of readers are connecting most with it? Have any audience questions surprised you? Has anyone pushed back on the idea of celebrating “defiance”? 5) What do you hope the audience learns after reading the book? If you had to boil it down, what should we be more skeptical of? How do we encourage against-the-grain thinking without sliding into cynicism? The SGEM will be back next episode with a structured critical appraisal of a recent publication. Our goal is to reduce the knowledge translation (KT) window from over 10 years to less than 1 year using the power of social media. So, patients get the best care, based on the best evidence. Remember to be skeptical of anything you learn, even if you heard it on The Skeptics’ Guide to Emergency Medicine. Previous SGEM Xtra Book Interviews SGEM Xtra – Brian Goldman: The Power of Kindness SGEM Xtra – Tim Caulfield: Illusion- What you Don't Know and Why It Matters SGEM Xtra – Steven Novella: The Skeptics Guide to the Universe  SGEM Xtra - Tim Caulfield: Relax – Damm It! SGEM Xtra - Mel Herbert: The Extraordinary Power of Being Average SGEM Xtra - Brian Goldman: Casino Shift - Stories from an ER on the Edge (coming soon) SGEM Xtra - Darren McKee: Uncontrollable - The Threat of Artificial Superintelligence and the Race to Save the World

Reference: . Timing of repeat epinephrine to inform paediatric anaphylaxis observation periods: a retrospective cohort study. Lancet Child & Adolescent Health. July 2025 Dr. Kammeron Brissett Guest Skeptic: Dr. Kammeron Brissett is a pediatric emergency medicine fellow at Children’s National Hospital in Washington, DC. She completed her pediatrics residency and a chief year at Rainbow Babies and Children’s Hospital in Cleveland, Ohio. Her interests include injury prevention, social determinants of health, and advocacy. Case: A 7-year-old boy with a peanut allergy presents to the emergency department (ED) after eating a cookie at a birthday party. Shortly afterwards, he developed hives and wheezing. His parents gave him an epinephrine auto-injector to improve his symptoms. In the ED, he feels much better. His vital signs are normal, and his lungs are clear. He has no other gastrointestinal or cardiovascular symptoms. The parents tell you, “Unfortunately, we’ve been through this before. It’s not the first time he has accidentally eaten something that may have had some peanuts in it. Last time, we sat in the ED for a few hours before going home. It’s been a long day. Can we just go home now?” Background:  Anaphylaxis is a serious, potentially life-threatening systemic allergic reaction with a fast onset. It is a clinical diagnosis that should be considered when: Acute illness with skin/mucosal involvement and either respiratory compromise or reduced blood pressure/end-organ symptoms; or Two or more of the following occurring rapidly after exposure: skin/mucosal involvement, respiratory compromise, reduced blood pressure, or persistent gastrointestinal symptoms; or Reduced blood pressure after exposure to a known allergen for the patient. Early recognition and treatment with intramuscular epinephrine is crucial. Sometimes, even after initial symptom improvement with IM epinephrine, anaphylaxis symptoms can recur even without exposure to the known trigger. This is called a biphasic reaction and can happen up to 72 hours later. The SGEM discussed anaphylaxis and biphasic reactions 13 years ago on SGEM#57. The bottom line was that prolonged observation is likely unnecessary in patients whose symptoms resolve with therapy in the ED. Biphasic reactions are rare and can occur anywhere from 10 minutes up to 6 days. We already have problems with boarding and overcrowding. We can’t keep all patients with anaphylaxis for 6 days. So, when can we send them home? Traditionally, ED observation after anaphylaxis has been around 4 to 6 hours to monitor for biphasic reactions. The Resuscitation Council UK recommends a risk-stratified approach: A patient can be discharged after 2 hours when there’s a good response to a single dose of epinephrine, the symptoms have resolved, the child and family has another epinephrine autoinjector and knows how to use it, and has adequate supervision after discharge. They recommend at least 6 hours of observation if two IM doses of epinephrine were needed or there was a prior biphasic reaction. Finally, they recommend at least 12 hours observation if there was severe respiratory compromise, >2 doses of epinephrine, ongoing allergen absorption, late-night presentation/limited access to care, or difficult access to emergency services. The National Institute for Care and Health Excellence (NICE) is even a bit more conservative, recommending any child under age of 16 with suspected anaphylaxis be admitted. What about in the US? In the United States, the 2023 AAAAI/ACAAI Joint Task Force Practice Parameter (JTFPP) emphasizes individualized, risk-based observation and shared decision-making, noting that risk for biphasic reactions is higher with more severe initial reactions and when >1 dose of epinephrine is required. It also highlights that patients with a prompt, complete, and durable response to epinephrine may not always require activation of EMS or prolonged monitoring, underscoring tailored disposition planning.  Clinical Question: Among children treated with epinephrine for anaphylaxis, what is the timing and incidence of repeat epinephrine that could inform safe observation periods? Reference: . Timing of repeat epinephrine to inform paediatric anaphylaxis observation periods: a retrospective cohort study. Lancet Child & Adolescent Health. July 2025 Population: Children 6 months to 17 years presenting to 31 EDs (30 US, 1 Canada) with an acute allergic reaction treated with epinephrine from 2016 to 2019. Excluded: Transfers from outside facilities, ED medication-induced reactions, missing pre-ED symptom documentation; comorbidities requiring tailored management Intervention: ED observation following the first epinephrine dose and need for additional epinephrine Comparison: Comparisons were made across severity strata (no respiratory/cardiovascular involvement vs respiratory involvement only vs cardiovascular involvement). Outcome: Primary Outcome: Time from first to last epinephrine dose (repeat epinephrine as a proxy for clinically significant ongoing/recurrent reaction). Secondary Outcomes: Biphasic anaphylaxis and non-anaphylaxis, persistent anaphylaxis and non-anaphylaxis, refractory anaphylaxis, other return-care outcomes Trial: Multicenter retrospective cohort Authors’ Conclusions: “A 2-h observation period is probably safe for most children who present to an emergency department with an acute allergic reaction requiring epinephrine. A 4-h observation period might be enough for patients with cardiovascular involvement who appear well.”  Quality Checklist for Observational Study:   Did the study address a clearly focused issue? Yes Did the authors use an appropriate method to answer their question? Yes Was the cohort recruited in an acceptable way? Yes Was the exposure accurately measured to minimize bias? Unsure Was the outcome accurately measured to minimize bias?  Unsure Have the authors identified all-important confounding factors? Unsure Was the follow-up of subjects complete enough? Unsure How precise are the results? Unsure Do you believe the results? Yes Can the results be applied to the local population? Yes Do the results of this study fit with other available evidence? Yes Funding of the Study: National Center for Advancing Translational Sciences and The National Institute of Allergy and Infectious Diseases of the National Institutes of Health. The funders had no role in study design, data collection, data analysis, interpretation, or writing of paper. Two of the authors report receiving consultant fees. One is on the advisory board and gets stock options from biotech companies and royalty fees from the publisher. Results: They included 5,641 eligible children with a median age of 7.9 years, with slightly more males (56%). 4956 (88%) fulfilled the National Institute of Allergy and Infectious Diseases and Food Allergy and Anaphylaxis Network criteria for anaphylaxis. In that group, 1.5% met criteria for biphasic anaphylaxis and 10.7% had persistent anaphylaxis. 4.7% received repeat epi after 2 hours from initial dose. 1.9% received repeat epi dose after 4 hours. Patients with cardiovascular involvement had higher rates of biphasic anaphylaxis. Key Results: Around 95% of children can be safely discharged after 2 hours of observation without the need for additional epinephrine. Among all patients, 5% received a repeat dose of epinephrine after 115 minutes. There were differences in patients with or without respiratory or cardiovascular involvement. Primary Outcome: In the entire cohort, 4.7% received epi 2 hours after the initial dose, 1.9% received epi after 4 hours, 1.1% received epi after 6 hours, and 0.8% received epi after 8 hours.  Secondary Outcomes:  86 (1.5%) had biphasic anaphylaxis 236 (4.2%) had biphasic non-anaphylactic allergic reactions 605 (10.7%) had persistent anaphylaxis 1400 (24.8%) had persistent non-anaphylactic allergic reactions 118 (2.1%) had refractory anaphylaxis Diagnosis of Anaphylaxis  We mentioned that anaphylaxis is a clinical diagnosis, but it’s not always clear-cut. In this retrospective review, the authors used ICD-10 codes and chart reviews to determine whether patients experienced anaphylaxis. They included patients who were treated with intramuscular, subcutaneous, or intravenous epinephrine. Potential biases include selection bias, information bias, and misclassification bias. Not all the patients included in this study actually met criteria for anaphylaxis, which is acknowledged by the authors. Anaphylaxis Practice Guideline update in 2023 states, “treatment with epinephrine or clinical response to epinephrine should also not be used as a surrogate marker to establish a diagnosis of anaphylaxis because there are many cases in which patients receive epinephrine for milder reactions.” Some of these patients were included because authors reported that “the administration of epinephrine might have mitigated reaction progression.” Appendix Table 3, which examines interrater reliability for agreement on anaphylaxis identification, reports kappa values ranging from 0.68 to 0.76, indicating substantial agreement but not perfect agreement. Repeat Epinephrine  The primary outcome for this study was the time from first to last administration of epinephrine. We must be careful and state that this is not the equivalent of a biphasic reaction. The decision to administer a repeat dose of epinephrine is also not always clear-cut. It is pragmatic. The clinician may have decided to administer another dose of epinephrine despite the patient not meeting the exact definition of anaphylaxis or a biphasic reaction. Epinephrine may have been administered because the child exhibited concerning signs or symptoms. For example,

Date: February 13, 2026 Reference: Lang et al. Factors associated with emergency department length of stay in Alberta: a study of patient-, visit-, and facility-level factors using administrative health data. CJEM. 2026 Jan 29. Guest Skeptic: Dr. Paul Parks is an emergency physician from Medicine Hat, Alberta. He has been the President of the Alberta Medical Association (AMA) Section of Emergency Medicine for many years, the AMA Board of Directors for 9 years, and the Previous President of the Alberta Medical Association.  Paul has won the Canadian Association of Emergency Physicians (CAEP) National Teacher of the Year Award and the CAEP Alan Drummond National Advocacy Award. Case: A 78-year-old man with congestive heart failure (CHF) and chronic obstructive pulmonary disease (COPD) arrives at the emergency department (ED) by ground emergency medical services (EMS) at 15:30 with dyspnea and hypoxia. He’s triaged Canadian Triage and Acuity Scale (CTAS) 2, needs non-invasive ventilation (NIV), diuresis, labs, chest x-ray, and likely admission. The department is packed; multiple admitted patients are boarded in hallway spaces because inpatient beds are unavailable, and nursing assignments are stretched. The patient is placed in the “EMS-PARK” area, which is an extension of the waiting room, and part of a mandatory EMS offload policy. Workup is done while the patient is still technically in the waiting room. The workup and disposition decision happen within a few hours, but transfer to an inpatient bed doesn’t occur until 2-3 days later. Background: ED length of stay (LOS) can be considered a vital sign of ED operations and the broader acute-care system. When LOS rises, it often signals that the ED is no longer functioning as a short-stay diagnostic and stabilization unit but is serving as a buffer for upstream demand and downstream capacity issues. The consequences are not just operational (hallway beds, delayed assessments, delayed analgesia, delayed imaging), but also human. We covered a study that showed for older patients, one overnight stay in the ED waiting for an inpatient bed was associated with a 4% absolute increase in mortality (SGEM#424). In addition, increasing LOS can lead to clinician burnout and moral injury. LOS is also tricky because ED crowding is rarely a single-point failure within the ED. Modern crowding frameworks (often summarized as input–throughput–output) remind us that while ED processes matter, some of the most powerful determinants are output constraints. This is especially true when there is access block and inpatient bed scarcity. In other words, you can run an efficient front-end, but if admitted patients cannot be moved to inpatient beds, the system backs up, and ED LOS climbs. As one concrete example of the output challenges many provinces struggle with, in Alberta, 1/3 of our acute hospital capacity, or about 30%, can be occupied by Alternate Level of Care patients. These alternative level of care (ALC) patients have had their acute care needs met, but they cannot be safely discharged from the hospital without specific continuing care resources – home care, assisted living, or long-term care. We’ve talked about ED crowding on an SGEM Xtra. It covered some of the Zombie Ideas that have been circulating around for decades. The classic one is to blame non-urgent patients for using the ED. They are not responsible for ED crowding. Diverting non-urgent patients away can be dangerous and won’t solve the underlying problem. CAEP published a position statement on emergency department overcrowding in 2013. CAEP argued for nationally standardized performance benchmarks. The statement also called for system-level solutions to improve flow while recognizing that ED optimization alone cannot solve crowding without hospital-wide and community-wide action. While CAEP’s advocacy has influenced awareness, policy discussion, and accountability framing, significant problems continue into 2026. Clinical Question: Across Alberta ED visits, what patient-, visit-, and facility-level factors are associated with longer ED length of stay? Reference: Lang et al. Factors associated with emergency department length of stay in Alberta: a study of patient-, visit-, and facility-level factors using administrative health data. CJEM. 2026 Jan 29. Population: ED visits drawn from linked Alberta Health Services administrative data for 14 ED facilities in Alberta, covering May 2022 to March 2023. Exposures: Factors such as age, deprivation measures, EMS arrival, triage acuity (CTAS), primary care continuity, time/day patterns, and facility-level constraints, including emergency inpatient pressure and hospital occupancy; staffing signals (hours worked per nurse) were also examined. Comparison:Between levels of each exposure, typically relative to a reference category or per-unit change (hospital occupancy, EMS vs non-EMS arrival, different facility types, weekday vs weekend, etc.). Outcomes Primary Outcome:ED total length of stay (LOS). Secondary Outcomes: There were no clearly prespecified secondary outcomes; however, the analysis was stratified by disposition (admitted vs discharged vs other = LWBS, Left AMA, transferred, or died), which functions like a planned subgroup/stratified analysis rather than a distinct secondary endpoint. Type of Study: This is an observational cross-sectional study using population-based administrative data. Authors’ Conclusions: “ED length of stay is associated with modifiable factors, including hospital capacity constraints, hours worked per nurse, and healthcare access inequities. Addressing hospital occupancy, optimizing staffing, and improving care coordination across the patient trajectory—such as between the ED, inpatient units, and post-discharge services—may enhance ED efficiency and reduce prolonged stays. Our findings align with established frameworks describing ED overcrowding and support targeted, system-level interventions to improve the efficiency of emergency care.” Quality Checklist for Observational Studies (Yes/No/Unsure) Did the study address a clearly focused issue? Yes Did the authors use an appropriate method to answer their question? Yes Was the cohort recruited in an acceptable way? Unsure Was the exposure accurately measured to minimize bias? Unsure Was the outcome accurately measured to minimize bias? Unsure Have the authors identified all-important confounding factors? No Was the follow-up of subjects complete enough? N/A How precise are the results? Very precise due to a large sample size, resulting in narrow confidence intervals for several of the point estimates. Do you believe the results? Yes  Can the results be applied to the local population? Unsure Do the results fit with other available evidence? Yes Who funded the trial? The authors acknowledge support under the Alberta Atlas of Healthcare Variation initiative. Did the authors declare any conflicts of interest? Brian R. Holroyd was the Senior Medical Director of the Emergency Strategic Clinical Network of Alberta Health Services at the start of this work. Matthew Pietrosanu was employed by Alberta Health Services for statistical consulting, technical writing, and general advising in the Alberta Atlas of Healthcare Variation initiative, which was expanded to include the preparation of this manuscript. Results: The dataset included 587,419 ED visits. The median age was 38 years, and 52% were female.  Most patients were discharged (68%), with 18% being admitted and 14% left without being seen, left AMA, transferred, or died. The median ED LOS was 3.1 hours overall, and LOS differed substantially by disposition (admitted patients had a much longer median LOS than discharged patients). Key Result: Facility- and system-level constraints were strongly associated with ED LOS, especially among admitted patients. The more emergency inpatient hours and higher hospital occupancy were associated with longer stays. Primary Outcome: Across all disposition categories, several patient-level factors were consistently associated with longer ED LOS, including older age, higher material or social deprivation, and arrival by EMS (ground or air). At the visit level, higher triage acuity and certain temporal factors (weekend admissions) were also associated with prolonged LOS, particularly among admitted patients. However, the largest and most clinically meaningful associations were at the facility level. Measures of hospital capacity strain dominated the results. Higher hospital inpatient occupancy and a greater number of emergency inpatients boarding in the ED were strongly associated with longer LOS, especially for admitted patients. For admitted patients, a one–standard deviation increase in hospital occupancy (approximately 0.11) was associated with a 17% increase in ED LOS, an effect size that dwarfed most patient- and visit-level predictors. This finding strongly supports the concept of access block (outflow from the ED) as the primary driver of prolonged ED stays. Higher hours worked per nurse were associated with shorter ED LOS in initial models, suggesting a potential staffing effect. However, this association disappeared after accounting for facility-level clustering, indicating that staffing effects may reflect broader organizational or structural differences between hospitals rather than a simple linear relationship with nursing hours. 1) Cross-Sectional Design & Temporality: The biggest design constraint is that this is a cross-sectional observational analysis. Exposures and outcomes are assessed within the same time frame. This means the direction of association can be unclear and may be difficult to determine. 2) Selection Bias: Although the dataset is large, it is not all Alberta EDs.

Date: January 3, 2026  Reference: Shroyer et al. Accuracy of cath lab activation decisions for STEMI-equivalent and mimic ECGs: Physicians vs. AI (Queen of Hearts by PMcardio). Am J Emerg Med. 2025 Nov. Guest Skeptic: Dr. Amal Mattu has been on the faculty at the University of Maryland since 1996. He has developed an academic niche in emergency cardiology and electrocardiography, and he also enjoys teaching and writing on other topics, including emergency geriatrics, faculty development, and risk management. Amal is currently a tenured professor and Vice Chair of Emergency Medicine at the University of Maryland School of Medicine, and a Distinguished Professor of the University of Maryland-Baltimore. Case: A 58-year-old man with diabetes and hypertension arrives at the emergency department (ED) 30 minutes after the sudden onset of substernal chest pressure radiating to the left arm, now improved to 3/10. His vital signs are BP 146/88, HR 92, RR 18, O2 sat 98% on room air. The initial 12-lead ECG shows RBBB with left anterior fascicular block and subtle anterior ST‑depression with proportionally tall, broad T waves in V2 to V4. This is an appearance that can be seen with Hyper-Acute T Wave Occlusive Myocardial Infarction (HATW‑OMI) or an ST-Elevated Myocardial Infarction (STEMI)‑mimic in conduction disease. A debate ensues between emergency medicine and cardiology on whether to activate the cath lab now or get troponins plus serial ECGs? Background: Emergency physicians need to be experts at interpreting ECGs. For decades, we’ve been taught STEMI criteria, only to learn repeatedly that important exceptions exist (posterior OMI, de Winter, hyperacute T waves, modified Sgarbossa in LBBB, etc.). Those exceptions have evolved into two distinct categories. There are the STEMI‑equivalents (OMI without classic ST‑elevation) and STEMI‑mimics (ST‑elevation without OMI). That expanding exception list increases diagnostic complexity and uncertainty. This is the area where artificial intelligence (AI), utilizing computer vision and machine learning, could provide a benefit. ECG-specific AI models now aim squarely at this problem. The study we are reviewing today evaluated the Queen of Hearts (QoH) AI. It is a deep neural network trained to detect occlusive myocardial infarction (OMI) on 12-lead ECGs. The model is described as “91% accurate” in prior work and is undergoing FDA review as of March 24, 2025, but whether it outperforms practicing clinicians on the hardest cases (STEMI‑equivalents and mimics) remained unclear. ECG diagnostic accuracy is important in emergency medicine because misclassification cuts both ways. Missed OMI delays reperfusion, while overcalls send patients and teams to the cath lab unnecessarily, putting patients at risk and using up valuable resources. A diagnostic aid that catches true positive OMIs while reducing false activations could improve outcomes and team throughput. Clinical Question: Among EM physicians and cardiologists interpreting STEMI‑equivalent and STEMI‑mimic ECGs, how accurate are they compared with a machine‑learning ECG algorithm? Reference: Shroyer et al. Accuracy of cath lab activation decisions for STEMI-equivalent and mimic ECGs: Physicians vs. AI (Queen of Hearts by PMcardio). Am J Emerg Med. 2025 Nov. Population: 53 emergency physicians and 42 cardiologists from a community system. Intervention: Human interpretation and QoH AI algorithm classifying each ECG as OMI requiring immediate CLA vs not Comparison (Reference Standard): OMI Present: Angiographic culprit with ≤TIMI II flow and elevated troponin, or culprit with TIMI III flow and significantly elevated troponin. OMI Absent: No culprit ≥50% stenosis on angiography or, when no angiography, negative serial troponins, no new echo wall‑motion abnormality, and negative clinical follow-up Outcome: Diagnostic accuracy of ECG-based CLA decisions. CLA‑positive was defined a priori for STEMI/STEMI‑equivalents and for “reperfused OMI” (Wellens, transient STEMI). Type of Study: A cross-sectional diagnostic accuracy study using a fixed case‑set, with comparisons to a reference standard. Authors’ Conclusions: “Physicians frequently misinterpret STEMI-equivalent and STEMI-mimic ECGs, potentially impacting CLA decisions. QoH AI demonstrated superior accuracy, suggesting a potential to reduce missed OMIs and unnecessary catheterization laboratory activations. Prospective studies are needed to validate these findings in clinical practice.”  Quality Checklist for a Diagnostic Study: The clinical problem is well-defined. Yes The study population represents the target population that would normally be tested for the condition (ie no spectrum bias). No The study population included or focused on those in the ED. No The study participants were recruited consecutively (i.e. no selection bias). No The diagnostic evaluation was sufficiently comprehensive and applied equally to all patients (i.e. no evidence of verification bias). No All diagnostic criteria were explicit, valid and reproducible (i.e. no incorporation bias). Unsure The reference standard was appropriate (i.e. no imperfect gold-standard bias). Yes/No  All undiagnosed patients underwent sufficiently long and comprehensive follow-up (i.e. no double gold-standard bias). No The likelihood ratio(s) of the test(s) in question are presented or can be calculated from the information provided. Yes The precision of the measure of diagnostic performance is satisfactory. Reasonable Funding and Conflicts of Interest. No external funding. Several authors report stock ownership/consulting with Powerful Medical (QoH developer), and other authors reported no conflicts. Results: They recruited 95 physicians to interpret the ECGs. There were 53 EM physicians and 42 cardiologists (23 general, 15 interventional, 4 EP electrophysiology). Experience: EPs 7 years (IQR 3 to 15) vs cardiologists 15 years (IQR 9.2 to 21). Key Result: QoH AI had significantly higher accuracy than humans, and there was no significant difference between EM and cardiologists. Primary Outcome: EM Physicians 65.6% (95% CI ~51 to 78) Cardiologists 65.5% (95% CI ~51 to 77) QoH AI 88.9% (95% CI 82 to 93) The most frequently misclassified by humans were LBBB (±OMI), transient STEMI, HATW‑OMI, and de Winter. QoH AI missed LBBB‑OMI and LV aneurysm. RBBB + fascicular block and HATW‑OMI produced the largest EP-cardiologist disagreement. 1) Spectrum Bias: The investigators intentionally selected “ambiguous” STEMI‑equivalent and STEMI‑mimic ECGs and fixed the OMI prevalence at 50% for the reader study. That design improves efficiency in comparing readers and the AI, but it does not reflect the spectrum or prevalence we see in day-to-day ED practice and therefore threatens external validity. In diagnostic accuracy research, spectrum bias occurs when the distribution of disease/non-disease, disease severity, or look-alikes in the sample differs from that in the clinical population in which the test will be used. It can change sensitivity and specificity in either direction. Selecting borderline cases may deflate both compared with routine practice, and it will certainly distort PPV/NPV because predictive values are prevalence‑dependent. The authors acknowledge this by noting the 50% OMI prevalence and the deliberate use of ambiguous ECGs “may not accurately reflect predictive values observed in real-world settings.” 2) Differential Verification & Imperfect Gold Standard: Not every patient had the same reference standard. While most OMI determinations used angiography, some mimic cases without angiography were adjudicated by serial troponins, echocardiography, and clinical follow-up. Using different reference standards in different subgroups constitutes differential verification (double gold‑standard) bias and can bias sensitivity and specificity up or down, depending on whether the disease can resolve or only become detectable over time. In addition, any composite or clinical adjudication process is an imperfect gold standard, which can either inflate or deflate the index test’s performance depending on how errors correlate across tests. The authors explicitly note these issues in their discussion. 3) Incorporation/Review Bias: The paper reports that cardiologists performing angiography were not masked to the ECG. When the result of (or information from) the index test helps determine the reference diagnosis, that is incorporation (review) bias. This typically inflates both sensitivity and specificity of the index test because the gold standard classification is partially “contaminated” by the test under study. In this context, seeing a concerning ECG may tilt the invasive assessment and adjudication toward “culprit” lesion labelling or influence borderline calls, making ECG-based classification look better than it truly is. 4) Unit‑of‑analysis & Precision Limitations: This was a reader study with 95 clinicians classifying the same small set of 18 ECGs. Even with appropriate statistics, the small number of cases means performance estimates can be fragile, and the 95% confidence intervals reflect that imprecision. To their credit, the authors modelled accuracy with multi-level robust variance to account for clustering (multiple readers rating the same cases), but the design still limits precision and generalizability across the full morphology spectrum of each category. The authors themselves state that “one representative ECG per type…cannot represent all ST‑T variants”, and that asking physicians to read far more than 18 tracings was impractical. This imprecision concerns should raise our skeptical radar, and we should factor this into our study interpretation. 5) External Validity: The study is single-center and uses an online survey without the interruptions, time pressure, serial ECGs,

Date: January 6, 2026  Guest Skeptic: Darren McKee is an author and speaker. He has served as a senior policy advisor and policy analyst for over 17 years. Darren hosts the international award-winning podcast, The Reality Check. He is also the author of an excellent, thought-provoking book called Uncontrollable: The Threat of Artificial Superintelligence and the Race to Save the World (2023). The book lays out what AI is, why advanced systems could pose real risks, and what individuals and institutions can do to increase AI safety.  We have discussed AI on the SGEM a few times: SGEM Xtra: Rock, Robot Rock – AI for Clinical Research SGEM#459: Domo Arigato Misuta Roboto – Using AI to Assess the Quality of the Medical Literature SGEM#460: Why Do I Feel Like, Somebody’s Watching Me – CHARTWatch to Predict Clinical Deterioration SGEM#472: Together In Electric Dreams – Or Is It Reality? AI already touches the emergency medicine world through triage, documentation (AI scribes), imaging, and patient communications. You argue in the book that we’re in exponential times, AI capabilities may accelerate, and that simple rules won’t reliably constrain advanced systems. All of which has implications for safety, bias, reliability, and public trust in healthcare.  The book is divided into three sections. I expanded on that so I could ask Daren questions about five different areas. Listen to the SGEM Xtra podcast to hear his responses: Five Questions for Darren Origin Story & Stakes: The book's introduction contrasts the confident historical skepticism about nuclear power with the speed with which reality overtook it. Give us a brief history of nuclear power. Then the book pivots to today’s AI and uses an analogy of humanity’s "smoke detector " moment. Explain what that is and why you decided now was the time to write this book. Part I: What is Happening? In the first part of the book, you build a narrative from AI to AGI to ASuperI. Can you provide some definitions of those terms and explain why they matter? Can you walk us through how current systems (large language models and image models) work at a high level? Why did emergent capabilities surprise even their builders, and why don’t we fully understand what’s happening under the hood of these machines? Part II: What are the Problems? You outline six core challenges: exponential progress, uncertain timelines (and expert disagreement), the alignment problem, why simple rules (à la “Three Laws”) fail, how control erodes as tech integrates into our lives, and how all this aggregates into societal risk. We are not going to go through all six, but could you explain the alignment problem? The other topic I wanted to expand on was the Three Laws. Part III: What Can We Do? The last two chapters get practical and discuss what institutions can do for safe AI innovation and what individuals can do to increase AI safety. Give us your top 2 or 3 institutional moves (transparency, evaluation, guardrails). How about your top 2 to 3 personal moves that listeners can do?  AI in the Emergency Department: Bring it home for us in the emergency department if you can. When an AI-enabled tool is proposed for triage, documentation, or image support, what are the three questions every emergency clinician or leader should ask before adoption?  The SGEM will be back next episode with a structured critical appraisal of a recent publication. Our goal is to reduce the knowledge translation (KT) window from over 10 years to less than 1 year using the power of social media. So, patients get the best care, based on the best evidence.   Remember to be skeptical of anything you learn, even if you heard it on the Skeptics’ Guide to Emergency Medicine.

Reference: Aronson PL, et al. Prediction Rule to Identify Febrile Infants 61–90 Days at Low Risk for Invasive Bacterial Infections. Pediatrics. September 2025 Date: January 6, 2026 Dr. Jillian Nickerson Guest Skeptic: Dr. Jillian Nickerson is a pediatric emergency medicine attending at Children’s National Hospital and Assistant Professor of Pediatrics and Emergency Medicine at The George Washington University School of Medicine and Health Sciences in Washington, DC. Prior to completing her PEM fellowship, she completed an emergency medicine residency at Mount Sinai in New York. Now she is also the associate program director for the pediatric emergency medicine fellowship program at Children’s National Hospital. Background: Fever is a common complaint that we encounter in the emergency department. In general, we want to be careful in our counseling and our practice not to perpetuate many of the myths and misconceptions that contribute to fever phobia. But there are certain populations where fever does get us a bit worried. When infants present with fever, we have to think about evaluating for other sources of infection such as bacteremia or meningitis, termed invasive bacterial infections (IBI). Fortunately, the prevalence of IBI tends to be low, but missing one could lead to significant morbidity or mortality. How do we determine whom to test and what tests to perform? We’ve covered multiple clinical decision rules for risk-stratifying febrile infants before on the SGEM: SGEM #171: Step-by-Step Approach to the Febrile Infant SGEM#296: She’s Got the Fever but Does She Need an LP, Antibiotics or an Admission? SGEM#341: Are the AAP Guidelines for the Evaluation and Management of the Well-Appearing Febrile Infant SGEM#387: Lumbar Punctures in Febrile Infants with Positive Urinalysis SGEM #474: Help! Which Clinical Decision Aid Should I Use to Risk Stratify Febrile Infants? Some of these clinical decision rules like Step by Step can be applied to infants up to 90 days. Others like the 2021 American Academy of Pediatrics (AAP) clinical practice guideline and the Pediatric Emergency Care Applied Research Network (PECARN) clinical decision rule, only include infants up to 60 days. Clinical Question: Is there an accurate prediction rule to identify well-appearing febrile infants 61–90 days old who are at low risk for invasive bacterial infection (IBI)? Reference: Aronson PL, et al. Prediction Rule to Identify Febrile Infants 61–90 Days at Low Risk for Invasive Bacterial Infections. Pediatrics. September 2025 Population: Non-ill-appearing febrile infants 61–90 days who had evaluation with both urinalysis/urine dipstick and blood culture Excluded: infants who were critically ill (ESI level 1, intubated, received vasoactive medication), death in the ED, prematurity ≤32 weeks, substantial pre-existing medical or surgical conditions, skin or soft tissue infections, home antibiotic use before ED visit Intervention: Derivation of a clinical prediction rule using urinalysis, temperature, ANC, ± procalcitonin. Comparison: none Outcome: Primary Outcome: Accuracy of the prediction rule to identify infants at low risk for IBI, defined as bacteremia or bacterial meningitis. Secondary Outcomes: none Trial: Retrospective cohort study Dr. Nathan Kuppermann Dr. Paul Aronson Authors: Dr. Paul Aronson is a pediatric emergency medicine attending and Professor of Pediatrics and Emergency Medicine at Yale School of Medicine. He is the Deputy Director of the Pediatric Residency Program and leads the Research Track. Dr. Nathan Kuppermann is executive vice president, chief academic officer of Children's National Hospital and director of the Children's National Research Institute. He also serves as chair of the Department of Pediatrics and associate dean of Pediatric Academic Affairs at the George Washington University School of Medicine and Health Sciences. Dr. Kuppermann is a pediatric emergency medicine physician, clinical epidemiologist and leader in emergency medical services for children. Authors’ Conclusions: We derived two accurate clinical prediction rules to identify febrile infants 61–90 days at low risk for invasive bacterial infections when urine and blood testing are obtained. Prospective validation is needed. Quality Checklist for Clinical Decision Rules: The study population included or focused on those in the ED. Yes Where was the study conducted (external validity). Conducted across 17 EDs in the PECARN Registry over 10 health systems (with many pediatric EDs). The patients were representative of those with the problem. Unsure. All important predictor variables and outcomes were explicitly specified. Yes This is a prospective, multicenter study including a broad spectrum of patients and clinicians (level II). No Clinicians interpret individual predictor variables and score the clinical decision rule reliably and accurately. Yes Is this an impact analysis of a previously validated CDR (level I study)? No For Level I studies, impact on clinician behavior and patient-centric outcomes is reported. N/A The follow-up was sufficiently long and complete. Yes The effect was large enough and precise enough to be clinically significant. Unsure. Funding of the Study: Eunice Kennedy Shriver National Institute of Child Health and Human Development. No financial conflicts of interest. Did the authors declare any conflicts of interest? The authors reported no conflicts of interest to disclose. Results: They included 4,952 infants. The median age was 72 days, and 54% male. Median maximum qualifying temperature was 38.7°C. Urinalysis was positive in 18%, LP/CSF testing was performed in 10%, antibiotics were given in 26%, and 34% were hospitalized. Approximately 100 (2%) tested positive for IBI with 95 cases of bacteremia and 5 cases of bacterial meningitis. A little bit over half (57%) with bacteremia also had UTI. Of those infants 1207 (24%) had procalcitonin and absolute neutrophil count (ANC) measured. That group had 27 with IBIs with 2 being bacterial meningitis.  Low risk predictors: Procalcitonin <0.24 ng/mL ANC < 10,710 cells/mm3 Key Results: This clinical prediction rule for risk-stratifying febrile infants 61-90 days based on urine and temperature >38.9°C had a sensitivity of 86%, specificity of 58.9%, NPV of 99.5%, and -LR of 0.24, but still needs external validation. This decision rule missed 14 infants with IBIs (13 with bacteremia and one with Group B Strep meningitis). There was a second decision rule that included procalcitonin ≤0.24 ng/mL and ANC  ≤10,710 cells/mm3. The derivation sensitivity was 100% but dropped to 85.2% on cross-validation. The specificity was around 65-68%. NPV ranged from 99.5-100%, Negative likelihood ratio was 0.22. Tune in to the podcast to hear Drs. Aronson and Kuppermann answer our nerdy questions. Selection Bias This secondary analysis included only febrile infants aged 61-90 days who underwent both urine and blood testing. A total of 20,211 infants in that age range had fevers, but only 30% of them had urine and blood cultures obtained. It’s also mentioned that the included infants had higher maximum qualifying temperatures, more assigned ESI triage level 2, and received parenteral antibiotics or were hospitalized. It’s possible that these infants may have been deemed sicker than those who did not undergo testing. The study was unable to capture the clinical decision-making that determined which infants underwent testing and which did not. How do you think this selection bias could impact your results? Overfitting the Data  The PCT and ANC rule showed perfect sensitivity in derivation but lower sensitivity on cross-validation (4 false negatives). This is a pattern that may represent model instability especially when dealing with uncommon outcomes. Increasing model complexity can improve apparent performance in the derivation set but worsen performance in validation because of overfitting. What steps did you take to try to limit overfitting and what changes if any do you anticipate in making to this CDR for external validation? The “Original” PECARN  Although this new clinical decision rule has a high NPV, we must also recognize the limitation that the prevalence of IBI is low. As disease prevalence decreases, NPV increases. The study team did look at this with the “original” PECARN rule’s rounded cutoffs of procalcitonin ≤0.5 and ANC ≤4000 without urinalysis. The sensitivity was 100% (95% CI 87.2-100) and specificity was 49.7% (95% CI 46.8-52.6). This was in the supplemental section. While we’re waiting for external validation of this new clinical decision rule, why not use the tried-and-true existing clinical decision rule? One less thing with new cutoffs for inflammatory markers to remember right? 90 Days and Beyond!  The clinical decision rule in this study, if and when externally validated, would apply to infants up to 90 days. What about beyond that? There’s quite a bit of variation in practice when it comes to workup for infants 2-6 months presenting with fever to the emergency department. How do you approach the workup of infants over 90 days?  Prematurity Many of the existing clinical decision rules exclude infants born prematurely. In reality, we also encounter these patients in the ED. How do you approach the workup of a febrile premature infant?  Bonus Question: Respiratory Virus Testing You report that you were unable to assess the results of respiratory viral testing as a predictor because of missing data, but we do know that febrile infants with viral infections do seem to have lower prevalence of IBI compared to those without. In your clinical practice, how do you manage infants with viral symptoms?