Reporting of Conflicts of Interest in Meta-analyses of Trials of Pharmacological Treatments
Bibliographic record
Abstract
CONTEXT: Disclosure of conflicts of interest (COIs) from pharmaceutical industry study funding and author-industry financial relationships is sometimes recommended for randomized controlled trials (RCTs) published in biomedical journals. Authors of meta-analyses, however, are not required to report COIs disclosed in original reports of included RCTs. OBJECTIVE: To investigate whether meta-analyses of pharmacological treatments published in high-impact biomedical journals report COIs disclosed in included RCTs. DATA SOURCES AND STUDY SELECTION: We selected the 3 most recent meta-analyses of patented pharmacological treatments published January 2009 through October 2009 in each general medicine journal with an impact factor of at least 10; in high-impact journals in each of the 5 specialty medicine areas with the greatest 2008 global therapeutic sales (oncology, cardiology, respiratory medicine, endocrinology, and gastroenterology); and in the Cochrane Database of Systematic Reviews. DATA EXTRACTION: Two investigators independently extracted data on disclosed study funding, author-industry financial ties, and author employment from each meta-analysis, from RCTs included in each meta-analysis, and on whether meta-analyses reported disclosed COIs of included RCTs. RESULTS: Of 29 meta-analyses reviewed, which included 509 RCTs, only 2 meta-analyses (7%) reported RCT funding sources; and 0 reported RCT author-industry ties or employment by the pharmaceutical industry. Of 318 meta-analyzed RCTs that reported funding sources, 219 (69%) were industry funded; and 91 of 132 (69%) that reported author financial disclosures had 1 or more authors with pharmaceutical industry financial ties. In 7 of the 29 meta-analyses reviewed, 100% of included RCTs had at least 1 form of disclosed COI (pharmaceutical industry funding, author-industry financial ties, or employment), yet only 1 of these 7 meta-analyses reported RCT funding sources, and 0 reported RCT author-industry ties or employment. CONCLUSION: Among a group of meta-analyses of pharmacological treatments published in high-impact biomedical journals, information concerning primary study funding and author COIs for the included RCTs were only rarely reported.
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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 | 0.297 | 0.673 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.017 | 0.057 |
| Bibliometrics | 0.016 | 0.019 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.007 | 0.004 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 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, 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".