Discrepancies in self-reported financial conflicts of interest disclosures by physicians: a systematic review
Bibliographic record
Abstract
Background There is a high prevalence of financial conflicts of interest (COI) between physicians and industry. Objectives To conduct a systematic review with meta-analysis examining the completeness of self-reported financial COI disclosures by physicians, and identify factors associated with non-disclosure. Data sources MEDLINE, Embase and PsycINFO were searched for eligible studies up to April 2020 and supplemented with material identified in the references and citing articles. Data extraction and synthesis Data were independently abstracted by two authors. Data synthesis was performed via systematic review of eligible studies and random-effects meta-analysis. Main outcomes and measures The proportion of discrepancies between physician self-reported disclosures and objective payment data was the main outcome. The proportion of discrepant funds and factors associated with non-disclosure were also examined. Results 40 studies were included. The pooled proportion of COI discrepancies at the article level was 81% (range: 54%–98%; 95% CI 72% to 89%), 79% at the payment level (range: 71%–89%; 95% CI 67% to 89%), 93% at the authorship level (range: 71%–100%; 95% CI 79% to 100%) and 66% at the author level (range: 8%–99%; 95% CI 48% to 78%). The proportion of funds discrepant was 33% (range: 2%–77%; 95% CI 12% to 58%). There was high heterogeneity between studies across all five analyses (I2=94%–99%). Most undisclosed COI were related to food and beverage, or travel and lodging. While the most common explanation for failure to disclose was perceived irrelevance, a median of 45% of non-disclosed payments were directly or indirectly related to the work. A smaller monetary amount was the most common factor associated with nondisclosure. Conclusions Physician self-reports of financial COI are highly discrepant with objective data sources reporting payments from industry. Stronger policies are required to reduce reliance on physician self-reporting of financial COI and address non-compliance.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.054 | 0.224 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.011 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".