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Record W2889764137 · doi:10.12968/hmed.2018.79.9.516

Emergency medicine myths and misconceptions: evaluating the evidence

2018· article· en· W2889764137 on OpenAlexaff

Bibliographic record

VenueBritish Journal of Hospital Medicine · 2018
Typearticle
Languageen
FieldMedicine
TopicIntraocular Surgery and Lenses
Canadian institutionsVancouver General HospitalUniversity of British Columbia
Fundersnot available
KeywordsMedicineSkepticismAlternative medicineSet (abstract data type)MythologyMedical literatureAspirinParoxysmal supraventricular tachycardiaMEDLINEMedical practiceMedical emergencyIntensive care medicineTachycardiaAnesthesiaEpistemologyLawInternal medicine

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.010
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.604
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.061
GPT teacher head0.374
Teacher spread0.313 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2018
Admission routes1
Has abstractyes

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