Emergency medicine myths and misconceptions: evaluating the evidence
Bibliographic record
Abstract
Medical reversal is common, with rates of reversal of practices that were considered standard of care as high as 40%. Unfortunately, many standards of care are never tested, but instead are often promoted based on pathophysiological explanations or simply being long-established practices. Much of medical practice is based on dogma: a set of principles laid down by authority as incontrovertibly true. This article evaluates four commonly taught dogmatic practices in emergency medicine to determine if they are supported by the medical literature or are instead myths and misconceptions: (1) topical anaesthetics inhibit corneal healing, (2) treatment of myocardial infarction is MONA (morphine, oxygen, nitrates, aspirin), (3) children do not get sprains because their ligaments are stronger than bone, and (4) vagal manoeuvres for supraventricular tachycardia never work in adults. Medicine is changing all the time, and the best way to ensure that one is practicing medicine that is accurate, up to date and not prone to being reversed is to always be sceptical and to learn how to read and interpret the medical literature.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.218 | 0.602 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.020 | 0.010 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.012 | 0.015 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".