Association between time to progression and subsequent survival inceritinib-treated patients with advanced ALK-positive non-small-cell lung cancer
Bibliographic record
Abstract
OBJECTIVE: ) for the treatment of advanced anaplastic lymphoma kinase positive (ALK+) non-small-cell lung cancer (NSCLC). RESEARCH DESIGN AND METHODS: A pooled analysis was performed on 181 ASCEND-1 (phase I) and ASCEND-2 (phase II) patients who experienced disease progression while on ceritinib. TTP was assessed on its association with PPS in a Kaplan-Meier analysis and in Cox proportional hazard models, adjusted for clinical covariates. MAIN OUTCOME MEASURES: Main outcomes measured include TTP, PPS, and OS. RESULTS: Patients with TTP ≥6 months experienced significantly longer PPS compared to those with TTP <6 months (median: 9.8 vs. 6.5 months, log-rank p-value < .01). When TTP was assessed as a continuous variable, every 3 months of longer TTP was associated with a 21% lower hazard of death following progression (hazard ratio [HR]: 0.79, 95% confidence interval [CI]: 0.63-1.00; adjusted HR: 0.79, 95% CI: 0.64-0.99). This positive association translated into an OS benefit: each 3 months of longer TTP was associated with a lower hazard of death (adjusted HR: 0.46, 95% CI: 0.37-0.58). Median OS was 20.0 months for patients with TTP ≥6 months and was 10.9 months for patients with TTP <6 months. CONCLUSIONS: A longer duration of TTP after treatment with ceritinib was significantly associated with a longer duration of both PPS and OS.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".