Lung Adjuvant Cisplatin Evaluation (LACE): A pooled analysis of five randomized clinical trials including 4,584 patients
Bibliographic record
Abstract
7008 Background: Several recent trials have shown a benefit of adjuvant cisplatin-based chemotherapy on overall survival (OS) in patients with non-small cell lung cancer (NSCLC). The aim of the Lung Adjuvant Cisplatin Evaluation (LACE) is to identify treatment options associated with a higher benefit, or groups of patients benefiting more from adjuvant chemotherapy. Methods: Individual patient data were collected and pooled from the five largest trials (ALPI, ANITA, BLT, IALT and JBR10) of cisplatin-based chemotherapy in completely resected patients, conducted after the NSCLC-meta-analysis (BMJ 1995, update ongoing). The interactions between patient subgroups or treatment types and chemotherapy effect on OS were analysed using hazard ratios (HR) and logrank tests stratified by trial. Results: With a median follow-up of 5.1 years, the overall HR of death was 0.89 (95% confidence interval [CI]: 0.82–0.96; p<0.005) corresponding to a 5-year absolute benefit of 4.2% with chemotherapy. There was no heterogeneity of chemotherapy effect among trials. The benefit varied with stage (test for trend, p=0.046) with the HR for stage I-A 1.41 [95% CI: 0.96–2.09], stage I-B 0.93 [0.78–1.10], stage II 0.83 [0.73–0.95] and stage III 0.83 [0.73–0.95]. The effect of chemotherapy did not vary significantly (test for interaction, p=0.10) with the associated drugs: vinorelbine (HR=0.80 [0.70–0.91]) etoposide/vinca-alcaloide (0.93 [0.80–1.07]) or other (0.98 [0.84–1.14]). There was no interaction between chemotherapy and sex, age, planned radiotherapy or planned total dose of cisplatin. Conclusions: Adjuvant cisplatin-based chemotherapy improves survival in patients with NSCLC. This benefit depends on stage and is greatest in patients with stages II and III. Our analysis suggests that platinum-based adjuvant chemotherapy may not benefit stage I-A patients. Results of disease-free survival will be presented at the meeting. Supported by PHRC and LNLCC [Table: see text]
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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 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.020 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".