121 Overdiagnosis in the emergency department: a sharper focus – are we causing harm while trying to help?
Bibliographic record
Abstract
Overdiagnosis is well described in relation to cancer screening, mental health, and cardiovascular risk factors; however, it appears to be an overlooked area in Emergency Medicine. Overdiagnosis in the Emergency Department occurs when a person is given a diagnostic label based on their presenting symptoms or life experiences that would not have caused the person harm if left undiscovered. Patients come to the ED in distress with an expectation to receive answers and appropriate care for their current medical ailments. Emergency physicians pride themselves on being diagnosticians and certainly would not expect one of their main duties to result in harmful or unnecessary diagnostic labelling. The Emergency Department is an environment where hundreds of diagnoses are made each day, with the tools readily available to make these diagnoses. Physicians in the ED are often tasked with making timely clinical assessments, decisions, and diagnoses that can unintentionally result in overdiagnosis. Can the quest for a diagnosis be at cross purposes with the Hippocratic oath of primum non nocere? This session will explore the rarely discussed range of overdiagnosis issues that are relevant to Emergency Medicine by providing an overview of salient literature. Currently, there are gaps in the literature on overdiagnosis in Emergency Medicine. In addition to being overlooked in the literature, overdiagnosis in the ED has received limited attention at previous Preventing Overdiagnosis Conferences. In the past, the conferences highlighted the significance of the harms associated with unnecessary imaging. This session will expand the scope of those considerations by including various domains where overdiagnosis may have relevance to ED providers. Specifically, we will highlight three pertinent areas: anaphylaxis, subsegmental pulmonary embolism, and low-risk chest pain. The aim of this seminar is to spark reflection on the potential harms associated with providing certain diagnoses on clinical grounds alone and post-ED referral patterns for patients. As well, we will invite participants to engage in a discussion on future actions to lessen the impacts of overdiagnosis in ED care. We will propose the beginnings of a framework of interventions that will decrease the impact of overdiagnosis in the ED.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.037 | 0.012 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".