Effectiveness of First-Line Treatments in Metastatic Squamous Non-Small-Cell Lung Cancer
Bibliographic record
Abstract
Background: Commonly used first-line (1L) chemotherapies for patients with advanced squamous-cell lung cancer (SCC) include gemcitabine–platinum (GP), nab-paclitaxel–carboplatin (nabpc), and sb-paclitaxel–carboplatin (sbpc) regimens. However, no head-to-head trials have compared those treatments. In the present study, we compared the efficacy of 1L GP, nabpc, and sbpc in patients with SCC and in patients with SCC who subsequently received second-line (2L) immunotherapy. Methods: Medical records of patients who initiated the 1L treatments of interest between June 2014 and October 2015 were reviewed by 132 participating physicians. Kaplan–Meier curves were used to evaluate overall survival (os), progression-free survival (PFS), and treatment discontinuation (TD), and then Cox proportional hazards regression was used to compare the results between the cohorts. Results: Medical records of 458 patients with SCC receiving GP (n = 139), nabpc (n = 159), or sbpc (n = 160) as 1L therapy were reviewed. Median os was longer with nabpc (23.9 months) than with GP (16.9 months; adjusted hazard ratio vs. nabpc: 1.55; p < 0.05) and with sbpc (18.3 months; adjusted hazard ratio: 1.42; p = 0.10). No differences were observed in PFS (median PFS: 8.8, 8.0, and 7.6 months for GP, nabpc, and sbpc respectively; log-rank p = 0.76) or in TD (median TD: 5.5, 5.7, and 4.6 months respectively; p = 0.65). For patients who subsequently received 2L immunotherapy, no differences in os were observed (median os: 27.3, 25.0, and 23.0 months respectively; p = 0.59). Conclusions: In a nationwide sample of SCC patients, longer median os was associated with 1L nabpc than with GP and sbpc. Median os for all 1L agents considered was similar in the subgroup of patients who sequenced to a 2L immunotherapy.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".