Association between industry funding and statistically significant pro-industry findings in medical and surgical randomized trials.
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: Conflicting reports exist in the medical literature regarding the association between industry funding and published research findings. In this study, we examine the association between industry funding and the statistical significance of results in recently published medical and surgical trials. METHODS: We examined a consecutive series of 332 randomized trials published between January 1999 and June 2001 in 8 leading surgical journals and 5 medical journals. Each eligible study was independently reviewed for methodological quality using a 21-point index with 5 domains: randomization, outcomes, eligibility criteria, interventions and statistical issues. Our primary analysis included studies that explicitly identified the primary outcome and reported it as statistically significant. For studies that did not explicitly identify a primary outcome, we defined a "positive" study as one with at least 1 statistically significant outcome measure. We used multivariable regression analysis to determine whether there was an association between reported industry funding and trial results, while controlling for study quality and sample size. RESULTS: Among the 332 randomized trials, there were 158 drug trials, 87 surgical trials and 87 trials of other therapies. In 122 (37%) of the trials, authors declared industry funding. An unadjusted analysis of this sample of trials revealed that industry funding was associated with a statistically significant result in favour of the new industry product (odds ratio [OR] 1.9, 95% confidence interval [CI] 1.3-3.5). The association remained significant after adjustment for study quality and sample size (adjusted OR 1.8, 95% CI 1.1-3.0). There was a nonsignificant difference between surgical trials (OR 8.0, 95% CI 1.1-53.2) and drug trials (OR 1.6, 95% CI 1.1-2.8), both of which were likely to have a pro-industry result (relative OR 5.0, 95% CI 0.7-37.5, p = 0.14). INTERPRETATION: Industry-funded trials are more likely to be associated with statistically significant pro-industry findings, both in medical trials and surgical interventions.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.024 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it