Publication and Reporting Conduct for Pharmacodynamic Analyses of Tumor Tissue in Early-Phase Oncology Trials
Bibliographic record
Abstract
PURPOSE: In principle, nondiagnostic biopsies for pharmacodynamic (PD) studies are carried out to inform decision-making in drug development. Because such procedures have no therapeutic value, their ethical justification requires that results be published. We aimed to assess the frequency of nonpublication of PD data in early phase cancer trials and to identify factors that prevent full publication of data. METHODS: We identified a sample of early-phase cancer trials containing invasive nondiagnostic tissue procurement for PD analysis from American Society of Clinical Oncology and American Association for Cancer Research meeting abstracts published between 1995 and 2005. These trials were followed to publication to determine frequency of nonpublication of PD data. Corresponding authors on early-phase cancer trials using invasive nondiagnostic research procedures were also surveyed to identify factors preventing full publication of PD data. RESULTS: In a sample of 90 trials, 22.2% (20 trials) resulted in no trial publication. Of published trials expected to contain PD reports, 16 (17.8%) did not include any PD data, and 21 (23.3%) reported incomplete PD data. We surveyed 92 authors; nonpublication was regarded as a frequent occurrence, and the most commonly cited barrier to full publication of PD data was strategic considerations in publication (58.8% of responding authors). CONCLUSIONS: Our results suggest ways that investigators, study planners, and reviewers can improve the burden/knowledge value balance in PD studies.
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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 | 0.779 | 0.908 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.020 | 0.027 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".