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Record W4377088176 · doi:10.1136/bmj.p1035

Is a lack of diversity among clinical practice guideline authors contributing to health inequalities for patients?

2023· editorial· en· W4377088176 on OpenAlexaboutno aff
Julie K. Silver

Bibliographic record

VenueBMJ · 2023
Typeeditorial
Languageen
FieldSocial Sciences
TopicDiversity and Career in Medicine
Canadian institutionsnot available
Fundersnot available
KeywordsGuidelineDiversity (politics)InequalityClinical PracticeMedicineData scienceFamily medicineComputer scienceSociologyPathologyMathematics

Abstract

fetched live from OpenAlex

Is a lack of diversity among clinical practice guideline authors contributing to health inequalities for patients?Julie K. Silver associate professorassociate chair Clinical practice guidelines and other types of guidance documents are among the most important evidence based publications in medicine.Many clinical practice guidelines are disseminated beyond the borders of the country that produced them, and affect access to care, diagnostic work-up, and treatment interventions for billions of people worldwide.This is especially true if they are published or endorsed by influential organisations such as professional societies in the United States, United Kingdom, European Union, and Canada.

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 imitation

Not 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.

metaresearch head score (Codex)0.029
metaresearch head score (Gemma)0.125
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Incentives · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.971
Threshold uncertainty score0.152

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0290.125
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0050.003
Bibliometrics0.0050.003
Science and technology studies0.0040.004
Scholarly communication0.0110.006
Open science0.0050.002
Research integrity0.0240.023
Insufficient payload (model declined to judge)0.0140.006

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 head0.215
GPT teacher head0.526
Teacher spread0.311 · 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 source (direct Gemma or distilled Codex), not a consensus.

Study designNot applicable
DomainIncentives
GenreEditorial

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

Citations11
Published2023
Admission routes1
Has abstractyes

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