Relationships Between Authorship Contributions and Authors' Industry Financial Ties Among Oncology Clinical Trials
Bibliographic record
Abstract
PURPOSE To test the hypothesis that authors who play key scientific roles in oncology clinical trials, and who therefore have increased influence over the design, analysis, interpretation or reporting of trials, are more likely than those who do not play such roles to have financial ties to industry. METHODS Data were abstracted from all trials (n = 235) of drugs or biologic agents published in the Journal of Clinical Oncology between January 1, 2006 and June 30, 2007. Article-level data included sponsorship, age group (adult v pediatric), phase, single versus multicenter, country (United States v other), and number of authors. Author-level data (n = 2,927) included financial ties (eg, employment, consulting) and performance of key scientific roles (ie, conception/design, analysis/interpretation, or manuscript writing). Associations between performance of key roles and financial ties, adjusting for article-level covariates, were examined using generalized linear mixed models. Results One thousand eight hundred eighty-one authors (64%) reported performing at least one key role, and 842 authors (29%) reported at least one financial tie. Authors who reported performing a key role were more likely than other authors to report financial ties to industry (adjusted odds ratio [OR], 4.3; 99% CI, 3.0 to 6.0; P < .0001). The association was stronger among trials with, compared with those without, industry funding (OR, 5.0 [99% CI, 3.4 to 7.5] v OR, 2.5 [99% CI, 1.3 to 4.8]), but was present regardless of sponsorship. CONCLUSION Authors who perform key roles in the conception and design, analysis, and interpretation, or reporting of oncology clinical trials are more likely than authors who do not perform such roles to have financial ties to industry.
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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: Incentives · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
| gpt | MetaresearchResearch integrity Domain: Methods · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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 | 0.116 | 0.574 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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, 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".