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Record W3094817503 · doi:10.3399/bjgp20x713177

Financial conflict of interest among clinical practice guideline-producing organisations

2020· letter· en· W3094817503 on OpenAlexaff
Ainsley Moore, Sharon E. Straus, Joel Lexchin, Brett D. Thombs

Bibliographic record

VenueBritish Journal of General Practice · 2020
Typeletter
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicPharmaceutical industry and healthcare
Canadian institutionsSt. Michael's HospitalJewish General HospitalYork UniversityMcMaster University
Fundersnot available
KeywordsGuidelineEurosConflict of interestFinanceBusinessAccountingMedicinePublic relationsPolitical science

Abstract

fetched live from OpenAlex

Competing interests between biomedical industry objectives and the public good raise concerns that some clinical practice guidelines sponsored by industry may not serve patients or the public as they should.1,2 GPs and other guideline users are likely unaware of the extent of organisational financial conflict of interest (FCOI) due to industry sponsorship of guideline-producing organisations. Understanding how guidelines are susceptible to bias when such organisational FCOI is present and potential mitigating strategies helps critically appraise guidelines that are encountered in practice. Ioannidis has warned guideline users that some guideline-producing societies are ‘behemoth financial enterprises’ .3 The American Heart Association receives almost 200 million USD annually from corporate sponsors, and the European Society of Cardiologists receives over 45 million Euros annually, 77% of their 60 million Euro budget.3 Despite this, little attention has been paid to the extent to which guideline producers are dependent on industry funds and how infrequently this is disclosed. A 2016 study found that financial relationships between guideline producers and biomedical companies were disclosed in only 1% of 290 guidelines, even though …

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)0.007
metaresearch head score (Gemma)0.059
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesResearch integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.052
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.059
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0030.029
Insufficient payload (model declined to judge)0.0020.000

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 head0.591
GPT teacher head0.584
Teacher spread0.006 · 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 designNot applicable
Domainnot available
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

Citations3
Published2020
Admission routes1
Has abstractyes

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