A multicentered population-based analysis of outcomes of patients with metastatic renal cell carcinoma (mRCC) who do not meet eligibility criteria for clinical trials.
Bibliographic record
Abstract
353 Background: Clinical trials have strict eligibility criteria to maintain internal validity. These criteria exclude many patients to whom the trial results are later applied to in clinical practice. Patients that do not meet eligibility criteria are poorly characterized. Methods: mRCC patients treated with VEGF targeted therapy were retrospectively deemed ineligible for clinical trials (according to commonly used inclusion/exclusion criteria) if they had a Karnofsky Performance Status (KPS) < 70%, brain metastases, non-clear cell histology, hemoglobin ≤ 9 g/dL, creatinine > 2x the upper limit of normal, platelet count of < 100x103/uL, neutrophil count < 1500/mm3 or corrected calcium ≤ 12 mg/dL. Results: 894/2076 (43%) patients were deemed ineligible for clinical trials by the above criteria. Between ineligible versus eligible patients, the response rate, median progression free survival (PFS) and median overall survival of first-line targeted therapy were 21% vs 29%, 5.2 vs 8.8 months and 14.5 vs 28.8 months (all p < 0.0001), respectively. Second-line PFS (if applicable) was 3.2 months in the trial ineligible vs 4.4 months in the trial eligible patients (p = 0.0074). When adjusted by the Heng et al prognostic categories, the hazard ratio for death between trial ineligible vs trial eligible patients was 1.621 (95% CI = 1.431–1.836, p < 0.0001). If only KPS, brain metastases and non-clear cell histology were used as exclusion criteria, 672 (32%) patients were excluded and the results were similar. Conclusions: The number of patients that are ineligible for clinical trials is high and their outcomes are inferior. Designing more inclusive clinical trials for this “ineligible” patient population are needed. [Table: see text]
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".