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Record W3095454500 · doi:10.1136/bmj.m3808

Making pharmaceutical research and regulation work for women

2020· article· en· W3095454500 on OpenAlexaff
TK Sundari Ravindran, Yot Teerawattananon, Cara Tannenbaum, Lavanya Vijayasingham

Bibliographic record

VenueBMJ · 2020
Typearticle
Languageen
FieldMedicine
TopicSex and Gender in Healthcare
Canadian institutionsInstitute of Gender and HealthCanadian Institutes of Health Research
Fundersnot available
KeywordsWork (physics)Pharmaceutical industryEngineering ethicsPolitical scienceSociologyMedicineEngineeringPharmacologyMechanical engineering

Abstract

fetched live from OpenAlex

The legacy of male bias within pharmaceutical research, regulation, and commercialisation needs to be rectified, argue Sundari Ravindran and colleagues

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)0.080
metaresearch head score (Gemma)0.173
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Open science
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.997
Threshold uncertainty score0.424

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0800.173
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0030.002
Science and technology studies0.0130.049
Scholarly communication0.0280.031
Open science0.0030.016
Research integrity0.0370.041
Insufficient payload (model declined to judge)0.0140.007

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 head0.672
GPT teacher head0.590
Teacher spread0.082 · 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 designTheoretical or conceptual
DomainMethods
GenreCommentary

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

Citations56
Published2020
Admission routes1
Has abstractyes

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