Eligibility Criteria and Endpoints in Metastatic Renal Cell Carcinoma Trials
Bibliographic record
Abstract
OBJECTIVES: Treatments for metastatic renal cell carcinoma (mRCC) are often compared across trials, but trial eligibility criteria and endpoints differ. In an effort to better align trials, the Definition for the Assessment of Time to event Endpoints in CANcer trials (DATECAN) project published recommendations in 2015 to be used in mRCC clinical trial design. We analyzed mRCC trial criteria to determine if DATECAN's recommendations were followed. MATERIALS AND METHODS: We compared eligibility criteria across 29 phase 3 mRCC trials conducted between 2003 and 2019. We then evaluated endpoints used in 10 phase 3 trials activated between 2015 and 2019 to determine their compliance with DATECAN's recommendations. RESULTS: Among the 29 trials, performance status, renal function, and disease characteristics differed in terms of requirements and measures used. In terms of endpoints, the 10 trials did not entirely follow DATECAN's recommendations. In total, 7/10 trials' primary endpoint was progression-free survival (PFS) as recommended; 4/9 trials used PFS as an endpoint but did not publish their definition of PFS, and the 5 that did, included "death from any cause" instead of DATECAN's recommendation of "death from kidney cancer." CONCLUSIONS: Key eligibility criteria were somewhat inconsistent across the phase 3 mRCC trials studied. Endpoints in the newer trials did not align with DATECAN's recommendations. Not only is greater standardization needed to facilitate meta-analyses and cross-trial comparisons, but as evident from lack of adherence to DATECAN's recommendations, greater promotion and adoption of recommendations are needed to better harmonize trial design.
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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 | 0.666 | 0.763 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.013 |
| Bibliometrics | 0.008 | 0.012 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".