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Record W3134270762 · doi:10.1136/bmj.n370

Achieving greater independence from commercial influence in research

2021· article· en· W3134270762 on OpenAlexaff
Joel Lexchin, Lisa Bero, Courtney Davis, Marc‐André Gagnon

Bibliographic record

VenueBMJ · 2021
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicPharmaceutical industry and healthcare
Canadian institutionsCarleton UniversityYork UniversityUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsIndependence (probability theory)Clinical trialData scienceComputer scienceMedicinePathologyStatisticsMathematics

Abstract

fetched live from OpenAlex

As part of The BMJ’s campaign for greater independence from commercial influence in the creation and use of evidence, Joel Lexchin and colleagues outline some approaches to minimise bias in clinical trials

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)0.578
metaresearch head score (Gemma)0.726
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Incentives · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.422
Threshold uncertainty score0.521

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.5780.726
Meta-epidemiology (narrow)0.0020.004
Meta-epidemiology (broad)0.0040.007
Bibliometrics0.0080.007
Science and technology studies0.0130.043
Scholarly communication0.0440.034
Open science0.0080.057
Research integrity0.0550.087
Insufficient payload (model declined to judge)0.0100.007

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 head0.787
GPT teacher head0.673
Teacher spread0.114 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designNot applicable
DomainIncentives
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

Citations28
Published2021
Admission routes1
Has abstractyes

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