A Population Survival Kinetics Assessment of Extensive Small Cell Lung Cancer and Rationale for Maintenance Therapy
Bibliographic record
Abstract
Progression-free survival (PFS) and overall survival (OS) curves generally approximate first-order kinetics. On log-linear plots, convex curves with downward inflection (indicating late acceleration of progression/death) might arise from stopping effective therapies. We digitized published PFS/OS curves for etoposide/platinum-treated extensive small-cell lung cancer (SCLC) and other malignancies and replotted the curves log-linearly. Of 26 SCLC PFS curves, 21 (81%) were highly convex (with a marked late down-turn), and 26 (100%) were moderately or highly convex vs. 35/888 (4%) highly convex and 186 (21%) moderately/highly convex curves for other cancers (p < 0.0001). For SCLC, all 32 OS curves were moderately or highly convex vs. 87/363 (24%) that were moderately/highly convex for other cancers (p < 0.0001). The SCLC PFS curves had an initial downward inflection at a median of 3.1 months (around the completion of first-line chemotherapy), then a second inflection at 5.4 months, with further acceleration of progression. The median PFS half-life was 11.9 months while receiving treatment vs. 1.7 months after the second inflection point. Immunotherapy benefit appeared to be limited to 6–10% of the population. SCLC PFS/OS curves are more often convex than for other cancers, reflecting SCLC chemotherapy sensitivity but rapid progression following the completion of first-line chemotherapy. Effective maintenance strategies are needed.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".