Trial sponsorship and self-reported conflicts of interest in breast cancer radiation therapy: An analysis of prospective clinical trials
Bibliographic record
Abstract
PURPOSE: We aim to assess any association between study and self-reported conflict of interest (COI) or trial sponsorship in breast cancer radiation clinical trials. MATERIALS AND METHODS: We searched PubMed for all clinical trials (CTs) published between 09/2004 and 09/2014 related to breast cancer. We included only radiotherapy CTs with primary clinical endpoints. We classified eligible trials according to the funding source, presence or absence of conflict of interest, study conclusion and impact factor (IF). RESULTS: 1,603 CTs were retrieved. 72 randomized clinical trials were included for analysis. For-profit (PO), not for profit organization (nPO), none and not reported sponsorship rates were 9/72 (12.5%), 35/72 (48.6%), 1/72 (1.4%), 27/72 (37.5%), respectively. Present, absent or not reported COI were found in 6/72 (8.3%), 43/72 (59.7%) and 23/72 (32%) of the CTs, respectively. Conclusion was positive, neutral and negative in 57/72 (79.1%), 9/72 (12.5%) and 6/72 (8.4%) of the trials, respectively. Positive conclusion was reported in 33/44 (75%) funded trials (PO and nPO) and 5/6 (83.3%) CTs with reported COI. On univariate analysis no association with funding source (P=0.178), COI (P=0.678) or trial region (P=0.567) and trial positive conclusion was found. Sponsored trials (HR 4.50, 95CI-0.1.23-16.53;P=0.0023) and positive trials (HR 4.78, 95CI- 1.16-19.63;P=0.030) were more likely to be published in higher impact factor journals in the multivariate analysis. CONCLUSIONS: nPO funding was reported in almost 50% of the evaluated CTs. No significant association between study conclusion and funding source, COI or trial region was identified. Sponsored trials and positive trials were more likely to be published in higher impact factor journals.
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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 | 0.189 | 0.417 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.012 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".