Research on policy mechanisms to address funding bias and conflicts of interest in biomedical research: a scoping review
Bibliographic record
Abstract
BACKGROUND: Industry funding and author conflicts of interest (COI) have been consistently shown to introduce bias into agenda-setting and results-reporting in biomedical research. Accordingly, maintaining public trust, diminishing patient harm, and securing the integrity of the biomedical research enterprise are critical policy priorities. In this context, a coordinated and methodical research effort is required to effectively identify which policy interventions are most likely to mitigate against the risks of funding bias. Subsequently this scoping review aims to identify and synthesize the available research on policy mechanisms designed to address funding bias and COI in biomedical research. METHODS: We searched PubMed for peer-reviewed, empirical analyses of policy mechanisms designed to address industry sponsorship of research studies, author industry affiliation, and author COI at any stage of the biomedical research process and published between January 2009 and 28 August 2023. The review identified literature conducting five primary analysis types: (1) surveys of COI policies, (2) disclosure compliance analyses, (3) disclosure concordance analyses, (4) COI policy effects analyses, and (5) studies of policy perceptions and contexts. Most available research is devoted to evaluating the prevalence, nature, and effects of author COI disclosure policies. RESULTS: Six thousand three hundreds eighty five articles were screened, and 81 studies were included. Studies were conducted in 11 geographic regions, with studies of international scope being the most common. Most available research is devoted to evaluating the prevalence, nature, and effects of author COI disclosure policies. This evidence demonstrates that while disclosure policies are pervasive, those policies are not consistently designed, implemented, or enforced. The available evidence also indicates that COI disclosure policies are not particularly effective in mitigating risk of bias or subsequent negative externalities. CONCLUSIONS: The results of this review indicate that the COI policy landscape could benefit from a significant shift in the research agenda. The available literature predominantly focuses on a single policy intervention-author disclosure requirements. As a result, new lines of research are needed to establish a more robust evidence-based policy landscape. There is a particular need for implementation research, greater attention to the structural conditions that create COI, and evaluation of policy mechanisms other than disclosure.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | MetaresearchResearch integrity Domain: Incentives · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | low |
| gpt | MetaresearchResearch integrity Domain: Incentives · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | low |
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 | 0.259 | 0.588 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.043 | 0.042 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.016 | 0.021 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.010 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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.
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, unvalidatedLabeled directly by 2 models reading the full record.
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".