Population Survival Kinetics Derived from Clinical Trials of Potentially Curable Lung Cancers
Bibliographic record
Abstract
Using digitized data from progression-free survival (PFS) and overall survival Kaplan–Meier curves, one can assess population survival kinetics through exponential decay nonlinear regression analyses. To demonstrate their utility, we analyzed PFS curves from published curative-intent trials of non-small cell lung cancer (NSCLC) adjuvant chemotherapy, adjuvant osimertinib in resected EGFR-mutant NSCLC (ADAURA trial), chemoradiotherapy for inoperable NSCLC, and limited small cell lung cancer (SCLC). These analyses permit assessment of log–linear curve shape and estimation of the proportion of patients cured, PFS half-lives for subpopulations destined to eventually relapse, and probability of eventual relapse in patients remaining progression-free at different time points. The proportion of patients potentially cured was 41% for adjuvant controls, 58% with adjuvant chemotherapy, 17% for ADAURA controls, not assessable with adjuvant osimertinib, 15% with chemoradiotherapy, and 12% for SCLC. Median PFS half-life for relapsing subpopulations was 11.9 months for adjuvant controls, 17.4 months with adjuvant chemotherapy, 24.4 months for ADAURA controls, not assessable with osimertinib, 9.3 months with chemoradiotherapy, and 10.7 months for SCLC. For those remaining relapse-free at 2 and 5 years, the cure probability was 74%/96% for adjuvant controls, 77%/93% with adjuvant chemotherapy, 51%/94% with chemoradiation, and 39%/87% with limited SCLC. Relatively easy population kinetic analyses add useful information.
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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 | 0.054 | 0.123 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".