Prospective Randomized Phase II Parallel Study of Vinorelbine Maintenance Therapy versus Best Supportive Care in Advanced Non-Small Cell Lung Cancer.
Bibliographic record
Abstract
BACKGROUND: Maintenance strategy has been used to improve survival in non-small cell lung cancer (NSCLC). We investigated whether switch maintenance therapy with vinorelbine improved progression free survival (PFS) after first-line chemotherapy with gemcitabine plus carboplatin. MATERIALS AND METHODS: , day 1, 15) or the best supportive care until disease progression. RESULTS: A total of 100 patients were registered, of whom 34 had a non-progressive response to first-line chemotherapy and randomly received maintenance vinorelbine (n=19) or best supportive care (n=15). The hazard ratio of PFS in the vinorelbine group relative to the best supportive care group was 1.097 (95% confidence interval = 0.479-2.510; P-value =0.827). There was no significant difference between the overall survival for the two groups (P=0.068). CONCLUSION: Switch maintenance strategies are beneficial, but defining the right candidates for treatment is a problem. Moreover, the trial designs do not always reflect the real-world considerations. Switch maintenance therapy with vinorelbine, though had tolerable toxicity, did not improve PFS in patients with NSCLC. Therefore, other agents should be considered in this setting.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".