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Record W2108368831

Contributors to primary care guidelines: What are their professions and how many of them have conflicts of interest?

2015· article· en· W2108368831 on OpenAlexaffabout
G. Michael Allan, Roni Kraut, Aven Crawshay, Christina Korownyk, Ben Vandermeer, Michael R. Kolber

Bibliographic record

VenuePubMed · 2015
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicPharmaceutical industry and healthcare
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsGuidelineFamily medicineConflict of interestMedicineJurisdictionPrimary careRelevance (law)MEDLINEPolitical scienceLawPathology
DOInot available

Abstract

fetched live from OpenAlex

OBJECTIVE: To determine the professions of those who contribute to guidelines, guideline variables associated with differing contributor participation, and whether conflict of interest statements are provided in primary care guidelines. DESIGN: Retrospective analysis of the primary care guidelines from the Canadian Medical Association website. Two independent data extractors reviewed the guidelines and extracted relevant data. SETTING: Canada. MAIN OUTCOME MEASURES: Sponsors of guidelines, jurisdiction (national or provincial) of guidelines, the professions of those who contribute to guidelines, and the reported conflict of interest statements within guidelines. RESULTS: Of the 296 guidelines in the family medicine section of the CMA Infobase, 65 were duplicates and 35 had limited relevance to family medicine. Twenty did not provide contributor information, leaving 176 guidelines for analysis. In total, there were 2495 contributors (authors and committee members): 1343 (53.8%) non-family physician specialists, 423 (17.0%) family physicians, 141 (5.7%) nurses, 75 (3.0%) pharmacists, 269 (10.8%) other clinicians, 203 (8.1%) nonclinician scientists, and 41 (1.6%) unknown professions. The proportion of contributors from the various professions differed significantly between provincial and national guidelines, as well as between industry-funded and non-industry-funded guidelines (both P < .001). For provincial guidelines, 30.8% of contributors were family physicians and 37.3% were other specialists compared with 13.9% and 57.4%, respectively, for national guidelines. Of industry-funded guidelines, 7.8% of contributors were family physicians and 68.6% were other specialists compared with 19.4% and 49.9%, respectively, for non-industry-funded guidelines. Conflicts of interest were not reported in 68.9% of guidelines. When reported, conflict of interest statements were present for 48.6% of non-family physician specialists, 30.0% of pharmacists, 27.7% of family physicians, and 10.0% or less of the remaining groups; differences were statistically significant (P < .001). CONCLUSION: Non-family physician specialists outnumber all other health care providers combined and are more than 3 times more likely to contribute to primary care guidelines than family physicians are. Conflict of interest statements were provided in the minority of guidelines, and for guidelines in which conflict of interest statements were included, non-family physician specialists were most likely to report them. Guidelines targeted to primary care should have much more primary care and family medicine representation and include fewer contributors who have conflicts of interest.

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)0.019
metaresearch head score (Gemma)0.150
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Research integrity
Consensus categoriesnone
DomainCandidate signal: Evaluation · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.999
Threshold uncertainty score0.303

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0190.150
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0070.010
Science and technology studies0.0020.001
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.776
GPT teacher head0.530
Teacher spread0.247 · 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 designObservational
DomainEvaluation
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

Citations40
Published2015
Admission routes2
Has abstractyes

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