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Conflict of interest as ethical shorthand: understanding the range and nature of “non-financial conflict of interest” in biomedicine

2019· article· en· W2996289423 on OpenAlexafffund
Quinn Grundy, Christopher Mayes, Kelly Holloway, Sasha Mazzarello, Brett D. Thombs, Lisa Bero

Bibliographic record

VenueJournal of Clinical Epidemiology · 2019
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicPharmaceutical industry and healthcare
Canadian institutionsMcGill UniversityJewish General HospitalUniversity of Toronto
FundersNational Health and Medical Research CouncilFonds de Recherche du Québec - SantéCanadian Institutes of Health ResearchUniversity of Toronto
KeywordsBiomedicineConflict of interestRange (aeronautics)Political scienceEngineeringLawBiologyBioinformatics

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.121
metaresearch head score (Gemma)0.444
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Research integrity
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.985
Threshold uncertainty score0.638

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1210.444
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0040.033
Scholarly communication0.0070.011
Open science0.0040.006
Research integrity0.0150.022
Insufficient payload (model declined to judge)0.0070.002

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 head0.875
GPT teacher head0.680
Teacher spread0.195 · 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 designQualitative
DomainMethods
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

Citations72
Published2019
Admission routes2
Has abstractno

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