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Financial Conflicts of Interest in Clinical Practice Guidelines: A Systematic Review

2021· review· en· W3123195415 on OpenAlexaff
Sahar Tabatabavakili, Rishad Khan, Michael A. Scaffidi, Nikko Gimpaya, David Lightfoot, Samir C. Grover

Bibliographic record

VenueMayo Clinic Proceedings Innovations Quality & Outcomes · 2021
Typereview
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicPharmaceutical industry and healthcare
Canadian institutionsQueen's UniversityUniversity of TorontoSt. Michael's Hospital
FundersFerring Pharmaceuticals
KeywordsMedicineConflict of interestGuidelinePaymentFamily medicineClinical PracticeFinancePathology

Abstract

fetched live from OpenAlex

OBJECTIVE: To systematically evaluate the prevalence of disclosed and undisclosed financial conflicts of interest (FCOI) among clinical practice guidelines (CPGs). METHODS: In this systematic review, we ascertained the prevalence and types of FCOI for CPGs from January 1, 1980, to March 3, 2019. The primary outcome was the prevalence of FCOI among authors of CPGs. FCOI disclosures were compared between medical subspecialties and societies producing CPGs. RESULTS: Among the 37 studies including 14,764 total guideline authors, 45% had at least one FCOI. The prevalence of FCOI per study ranged from 6% to 100%. More authors had FCOI involving general payments (39%) compared with research payments (29%). Oncology, neurology, and gastroenterology had the highest prevalence of FCOI compared with other medical specialties. Among the 8 studies that included the monetary values in US dollars of FCOI, average payments per author ranged from $578 to $242,300. Among the 10 studies that included data on undisclosed FCOI, 32% of authors had undisclosed industry payments. CONCLUSION: Our study found a significant difference in FCOI prevalence based on types of FCOI and CPG sponsor society. Additional research is required to quantify the implications of FCOI on clinical judgment and patient care.

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)0.039
metaresearch head score (Gemma)0.252
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Research integrity
Consensus categoriesMetaresearch, Research integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.679
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0390.252
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0100.002
Bibliometrics0.0010.004
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0030.009
Insufficient payload (model declined to judge)0.0010.000

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 head0.926
GPT teacher head0.749
Teacher spread0.177 · 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; both teacher heads agree on what is shown here.

Study designSystematic review
Domainnot available
GenreReview

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
Published2021
Admission routes1
Has abstractyes

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