Carboplatin and Etoposide With or Without Palifosfamide in Untreated Extensive-Stage Small-Cell Lung Cancer: A Multicenter, Adaptive, Randomized Phase III Study (MATISSE)
Bibliographic record
Abstract
Purpose To evaluate the efficacy of the addition of palifosfamide to carboplatin and etoposide in extensive stage (ES) small-cell lung cancer (SCLC). Patients and Methods MATISSE was a randomized, open-label, adaptive phase III study. Previously untreated patients with ES SCLC were randomly assigned in a 1:1 fashion to receive carboplatin at area under the serum concentration-time curve 5 on day 1 plus etoposide 100 mg/m 2 per day on days 1 to 3 every 21 days (CE) or carboplatin at area under the serum concentration-time curve 4 on day 1 plus etoposide 100 mg/m 2 per day plus palifosfamide 130 mg/m 2 per day on days 1 to 3 every 21 days (PaCE). The primary end point was overall survival. Results In all, 188 patients were enrolled; 94 patients received CE and 94 patients received PaCE. The median age on both arms was 61 years. Six cycles of chemotherapy were completed on both arms of the study by approximately 50% of the patients. Serious adverse events were documented and did not differ significantly between patients receiving PaCE and those receiving CE. Median overall survival was similar between both arms with 10.03 months on PaCE and 10.37 months on CE ( P = .096). Conclusion The addition of palifosfamide to CE failed to improve survival in ES 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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".