Randomized Trial of Paclitaxel Plus Supportive Care Versus Supportive Care for Patients With Advanced Non-Small-Cell Lung Cancer
Bibliographic record
Abstract
BACKGROUND: In phase II trials, paclitaxel has been shown to have antitumor activity in patients with advanced non-small-cell lung cancer (NSCLC). However, the survival and quality-of-life (QOL) benefits of paclitaxel used as a single agent compared with supportive care alone have not been assessed in a randomized clinical trial. METHODS: A total of 157 patients with stage IIIB or IV NSCLC who had received no prior chemotherapy were randomly assigned to receive either best supportive care alone (78 patients) or paclitaxel plus supportive care (79 patients). Paclitaxel was administered as a 3-hour intravenous infusion every 3 weeks. Supportive care included palliative radiotherapy and supportive therapy with corticosteroids, antibiotics, analgesics, antiemetics, transfusions, and other symptomatic therapy as required. The primary end point of the study was survival. Time to disease progression, response rate, adverse events, and QOL were secondary end points. RESULTS: Pretreatment characteristics were evenly distributed between the two arms. Survival was statistically significantly better in the paclitaxel plus supportive care arm than in the supportive care alone arm (two-sided P =.037) (median survival = 6.8 months versus 4.8 months). Cox multivariate analysis showed paclitaxel plus supportive care to be statistically significantly associated with improved survival (two-sided P =.048). QOL was similar for both treatment arms, except for the functional activity score of the Rotterdam Symptom Checklist, where QOL data statistically significantly favored the paclitaxel plus supportive care arm (two-sided P =.043). CONCLUSION: The addition of paclitaxel to best supportive care significantly improved survival and time to disease progression compared with best supportive care in patients with advanced NSCLC and may improve some aspects of QOL.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".