Reporting practices of pharmacodynamic studies involving invasive research procedures in cancer trials
Bibliographic record
Abstract
BACKGROUND: Tumour biopsy for pharmacodynamic (PD) study is increasingly common in early-phase cancer trials. As they are non-diagnostic, the ethical justification for such procedures rests on their knowledge value. On the premise that knowledge value is related to reporting practices and outcome diversity, we assessed in a sample of recent invasive PD studies within cancer trials. METHODS: We assessed reporting practices and outcomes for PD studies in a convenience sample of cancer trials published from 2000 to 2010 that employed invasive, non-diagnostic tissue procurement. Extracted data were used to measure outcome reporting in individual trials. Using a reporting scale we developed for exploratory purposes, we tested whether reporting varied with study characteristics, such as funding source or drug novelty. RESULTS: Reporting varied widely within and across studies. Some practices were sporadically reported, including results of all planned tests (78% trials reporting), use of blinded histopathological assessment (43% trials reporting), biopsy dimensions (38% trials reporting), and description of patient flow through PD analysis (62%). Pharmacodynamic analysis as a primary end point and mandatory biopsy had statistically significant positive relationships with overall quality of reporting. A preponderance of positive results (61% of the studies described positive PD results) suggests possible publication bias. CONCLUSION: Our results highlight the need for PD-reporting guidelines, and suggest several avenues for improving the risk/benefit for studies involving invasive, non-diagnostic tissue procurement.
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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 | 0.787 | 0.885 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.012 | 0.014 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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, 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".