Estimating scenarios for survival time in patients with advanced melanoma receiving immunotherapy and targeted therapy
Bibliographic record
Abstract
BACKGROUND: We aim to provide survival scenario estimates for patients with advanced melanoma starting targeted therapies and immunotherapies. MATERIALS AND METHODS: We sought randomized trials of targeted therapies and immunotherapies for advanced melanoma and recorded the following percentiles (represented survival scenario) from each overall survival (OS) curve: 90th (worst-case), 75th (lower-typical), 50th (median), 25th (upper-typical), and 10th (best-case). We tested whether these scenarios can be estimated for each OS curve by multiplying its median by 4 multiples: 0.25 (worst-case), 0.5 (lower-typical), 2 (upper-typical), and 3 (best-case). RESULTS: We identified 15 trials with 8025 patients. For first-line combination targeted therapy treatment groups, the median (interquartile range, IQR) in months for each percentile was: 90th, 6.2 (6.0-6.5); 75th, 11.3 (11.3-11.4); and median, 24.4 (23.5-25.3). For the first-line combination immunotherapy treatment group, the percentiles in months were: 90th, 3.9 (2.8-4.5); 75th, 13.4 (10.1-15.4), median 73 (not applicable). In targeted therapy groups, simple multiples of the median OS were accurate for estimating the 90th percentile in 80%; 75th percentile in 40%; 25th percentile in 100%. In immunotherapy groups, these multiples were accurate at 0% for the 90th percentile, and 43% for the 75th percentile. The 90th percentile (worst-case scenario) was better estimated as 1/6× median OS, and the 75th percentile (lower-typical) as 1/3× median OS. CONCLUSIONS: Simple multiples of the median OS are a useful framework to estimate scenarios for survival for patients receiving targeted therapies, not immunotherapy. Longer follow-up is required to estimate upper-typical and best-case scenarios.
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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 | 0.026 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".