Reporting of financial conflicts of interest in meta-analyses of drug trials published in high-impact medical journals: comparison of results from 2017 to 2018 and 2009
Bibliographic record
Abstract
BACKGROUND: A previous study found that 2 of 29 (6.9%) meta-analyses published in high-impact journals in 2009 reported included drug trials' funding sources, and none reported trial authors' financial conflicts of interest (FCOIs) or industry employment. It is not known if reporting has improved since 2009. Our objectives were to (1) investigate the extent to which pharmaceutical industry funding and author-industry FCOIs and employment from included drug trials are reported in meta-analyses published in high-impact journals and (2) compare current reporting with results from 2009. METHODS: We searched PubMed (January 2017-October 2018) for systematic reviews with meta-analyses including ≥ 2 randomized controlled trials (RCTs) of patented drugs. We included 3 meta-analyses published January 2017-October 2018 from each of 4 high-impact general medicine journals, high-impact journals from 5 specialty areas, and the Cochrane Database of Systematic Reviews, as in the previous study. RESULTS: Among 29 meta-analyses reviewed, 13 of 29 (44.8%) reported the funding source of included trials compared to 2 of 29 (6.9%) in 2009, a difference of 37.9% (95% confidence interval, 15.7 to 56.3%); this included 7 of 11 (63.6%) from general medicine journals, 3 of 15 (20.0%) from specialty medicine journals, and 3 of 3 (100%) Cochrane reviews. Only 2 of 29 meta-analyses (6.9%) reported trial author FCOIs, and none reported trial author-industry employment. PROTOCOL PUBLICATION: A protocol was uploaded to the Open Science Framework prior to initiating the study. https://osf.io/8xt5p/ LIMITATIONS: We examined only a relatively small number of meta-analyses from selected high-impact journals and compared results to a similarly small sample from an earlier time period. CONCLUSIONS: Reporting of drug trial sponsorship and author FCOIs in meta-analyses published in high-impact journals has increased since 2009 but is still suboptimal. Standards on reporting of trial funding described in the forthcoming revised PRISMA statement should be adapted and enforced by journals to improve reporting.
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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 | 0.207 | 0.506 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.044 |
| Bibliometrics | 0.024 | 0.029 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".