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Record W3135645750 · doi:10.1503/cmaj.200651-f

Gestion des conflits d’intérêts durant l’élaboration de lignes directrices en santé

2021· article· fr· W3135645750 on OpenAlexaffvenueabout
Gregory Traversy, Lianne Barnieh, Elie A. Akl, G. Michael Allan, Melissa Brouwers, Isabelle Ganache, Quinn Grundy, Gordon Guyatt, Diane Kelsall, Gillian Leng, Ainsley Moore, Navindra Persaud, Holger J. Schünemann, Sharon E. Straus, Brett D. Thombs, Rachel Rodin, Marcello Tonelli

Bibliographic record

VenueCanadian Medical Association Journal · 2021
Typearticle
Languagefr
FieldMedicine
TopicClinical practice guidelines implementation
Canadian institutionsMcMaster UniversityOttawa Public HealthKellogg's (Canada)Jewish General HospitalMcMaster University Medical CentreCouncil of Ontario UniversitiesUniversity of CalgaryUniversité de MontréalInstitut National d'Excellence en Santé et en Services SociauxUniversity of Alberta
Fundersnot available
KeywordsHumanitiesPolitical scienceArtPhilosophy

Abstract

fetched live from OpenAlex

POINTS CLÉS Le public est de plus en plus sensible à l’importance de divulguer et de gérer les conflits d’intérêts (CI) liés à l’élaboration des guides de pratique clinique et des directives de santé publique, en raison de dossiers récents très médiatisés au Canada et à l’é

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)0.012
metaresearch head score (Gemma)0.151
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.745
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0120.151
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0170.001

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 head0.058
GPT teacher head0.399
Teacher spread0.342 · 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 designNot applicable
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

Citations3
Published2021
Admission routes3
Has abstractyes

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