Outcomes of Small Cell Lung Cancer Patients Treated With Cisplatin-Etoposide Versus Carboplatin-Etoposide
Bibliographic record
Abstract
PURPOSE: This descriptive study compares overall survival (OS) and locoregional control (LRC) rates between cisplatin-etoposide (EP) and carboplatin-etoposide (EC) at a population level in patients with limited disease (LD) and extensive disease (ED) small cell lung cancer (SCLC). MATERIALS AND METHODS: All patients diagnosed with SCLC from January 2004 to December 2008 were identified. Patients with LD SCLC treated with EP or EC and concurrent or sequential radiotherapy and those with ED SCLC treated with EP or EC were included for analysis. A retrospective review examining prognostic features and outcomes was performed. OS and LRC curves were calculated using the Kaplan-Meier method and compared with the log-rank test. RESULTS: A total of 249 patients with LD SCLC and 287 patients with ED SCLC were identified. Patients treated with EC were significantly older for both LD (median 62 vs. 72, P<0.001) and ED (median 62 vs. 73, P<0.001). Median follow-up times were 37 and 22 months for LD and ED SCLC, respectively. Median OS for EP and EC in LD SCLC patients were 23 and 18 months (P=0.10). LRC rates at 12 months were 81% for the EP group and 68% for the EC group (P=0.97). Median OS for the EP and EC patients with ED SCLC was 10 and 11 months, respectively, (P=0.24). CONCLUSION: Despite the preferential use of EC in an older population, median OS and LRC rates were not significantly different for patients treated with EP for both LD and ED SCLC.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".