Pooled Analysis of the Effect of Age on Adjuvant Cisplatin-Based Chemotherapy for Completely Resected Non–Small-Cell Lung Cancer
Bibliographic record
Abstract
PURPOSE: This pooled analysis was undertaken to assess the efficacy and toxicity of adjuvant cisplatin-based chemotherapy in elderly patients with non-small-cell lung cancer (NSCLC). METHODS: We used individual patient data from 4,584 patients enrolled onto five trials of cisplatin-based chemotherapy who form the basis for the Lung Adjuvant Cisplatin Analysis (LACE) pooled analysis. Patient and treatment characteristics, overall and event-free survival, cause-specific mortality, chemotherapy toxicity and delivery were compared among three age groups: 3,269 young (71%; < 65), 901 midcategory (20%; 65 to 69), and 414 elderly patients (9%; >or= 70). Log-rank tests stratified by trials were used with a test for trend to study the effect of chemotherapy on survival according to age. RESULTS: The hazard ratio (HR) of death for the young patients was 0.86 (95% CI, 0.78 to 0.94), 1.01 for the midcategory (95% CI, 0.85 to 1.21), and 0.90 for elderly patients (95% CI, 0.70 to 1.16; test for trend: P = .29). The HR for event-free survival was 0.82 for young (95% CI, 0.75 to 0.90), 0.90 for the midcategory (95% CI, 0.76 to 1.06), and 0.87 for elderly patients (95% CI, 0.68 to 1.11; test for trend: P = .42). More elderly patients died from non-lung cancer-related causes (12% young, 19% midcategory, 22% elderly; P < .0001). No differences in severe toxicity rates were observed. Elderly patients received significantly lower first and total cisplatin doses, and fewer chemotherapy cycles (chi(2) P < .0001). CONCLUSION: Adjuvant cisplatin-based chemotherapy should not be withheld from elderly patients with NSCLC purely on the basis of age.
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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 | 0.021 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.017 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".