Objective Responses in Patients with Malignant Melanoma or Renal Cell Cancer in Early Clinical Studies Do Not Predict Regulatory Approval
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
PURPOSE: Tumor responses in early-phase trials are used to determine whether new agents warrant further study. Given that spontaneous regressions are observed in melanoma and renal cell carcinoma, this study assessed whether tumor responses, particularly in these two tumor types, predict for future regulatory drug approval. EXPERIMENTAL DESIGN: The literature was reviewed to assess tumor response rates to cytotoxic agents in phase I and II trials in the following solid tumors: melanoma, renal cell carcinoma, non-small-cell lung cancer, breast cancer, ovarian cancer, colorectal cancer, and other solid tumors. Response rates were categorized and the relationship of these categories to the end point of regulatory drug approval was determined. RESULTS: Fifty-eight drugs were assessed in 100 phase I trials, and 46 of these drugs were also studied in 499 phase II trials. Higher overall response rates in both phase I trials (P = 0.03) and phase II trials (P < 0.0001) were predictive of regulatory approval. However, response in melanoma or renal cell carcinoma was not predictive for either phase I or phase II studies. CONCLUSIONS: For cytotoxic agents, although overall objective response rates reliably predict subsequent marketing approval, isolated responses in melanoma and renal cell carcinoma are not predictive.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.067 | 0.219 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.004 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.009 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it