Reporting of financial and non-financial conflicts of interest by authors of systematic reviews: a methodological survey
Bibliographic record
Abstract
BACKGROUND: Conflicts of interest may bias the findings of systematic reviews. The objective of this methodological survey was to assess the frequency and different types of conflicts of interest that authors of Cochrane and non-Cochrane systematic reviews report. METHODS: We searched for systematic reviews using the Cochrane Database of Systematic Reviews and Ovid MEDLINE (limited to the 119 Core Clinical Journals and the year 2015). We defined a conflict of interest disclosure as the reporting of whether a conflict of interest exists or not, and used a framework to classify conflicts of interest into individual (financial, professional and intellectual) and institutional (financial and advocatory) conflicts of interest. We conducted descriptive and regression analyses. RESULTS: Of the 200 systematic reviews, 194 (97%) reported authors' conflicts of interest disclosures, typically in the main document, and in a few cases either online (2%) or on request (5%). Of the 194 Cochrane and non-Cochrane reviews, 49% and 33%, respectively, had at least one author reporting any type of conflict of interest (p=0.023). Institutional conflicts of interest were less frequently reported than individual conflicts of interest, and Cochrane reviews were more likely to report individual intellectual conflicts of interest compared with non-Cochrane reviews (19% and 5%, respectively, p=0.004). Regression analyses showed a positive association between reporting of conflicts of interest (at least one type of conflict of interest, individual financial conflict of interest, institutional financial conflict of interest) and journal impact factor and between reporting individual financial conflicts of interest and pharmacological versus non-pharmacological intervention. CONCLUSIONS: Although close to half of the published systematic reviews report that authors (typically many) have conflicts of interest, more than half report that they do not. Authors reported individual conflicts of interest more frequently than institutional and non-financial conflicts of interest.
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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: Reporting · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | medium |
| gpt | MetaresearchResearch integrity Domain: Reporting · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | medium |
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 | 0.514 | 0.814 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.029 | 0.030 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 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, 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".