Authors' Self-Declared Financial Conflicts of Interest Do Not Impact the Results of Major Cardiovascular Trials
Bibliographic record
Abstract
OBJECTIVES: This study assessed whether the results of major, potentially practice-altering cardiovascular trials were influenced by the authors' self-declared financial conflicts of interest (FCOI). Secondary objectives included assessment of trial outcomes by source of funding, by FCOI subtype, and by trial endpoints. BACKGROUND: Financial conflicts of interest, ubiquitous in cardiovascular medicine because of significant investigator-industry collaborations, potentially can influence trial outcomes. METHODS: A MEDLINE search was performed using the MeSH term cardiovascular disease limited to randomized controlled trials and clinical trials published from January 1, 2000, through April 15, 2008, in 3 high-impact journals. Two reviewers independently abstracted data from the published article. Chi-square tests, Fisher exact tests, and multivariate logistic regression were used to assess the associations between FCOI and study characteristics and between FCOI and trial outcomes. RESULTS: Of the 550 articles reviewed, 51.1% satisfied FCOI criteria, including at least one of the following: stock ownership, employee, speaker's bureau, and consultant). Of the 538 articles providing sponsorship information, 34.6% reported funding solely by nonprofit organizations, 48.3% reported funding solely by industry, and 17.1% reported funding by a combination. Prevalence of FCOI significantly increased with level of industry funding: 21.5% (none), 50.0% (shared), 75.0% (industry solely, n = 281, p < 0.0001). However, no differences in reporting of favorable results were detected when articles were analyzed by self-declared FCOI (60.5% vs. 59.5% in those with and without, odds ratio: 1.04, p = 0.81). This result was upheld in multivariate analysis. CONCLUSIONS: Authors' self-declared FCOI and source of funding do not seem to impact outcomes in major cardiovascular clinical trials.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.043 | 0.009 |
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".