Postoperative Radiotherapy for Pathologic N2 Non–Small-Cell Lung Cancer Treated With Adjuvant Chemotherapy: A Review of the National Cancer Data Base
Bibliographic record
Abstract
PURPOSE: To investigate the impact of modern postoperative radiotherapy (PORT) on overall survival (OS) for patients with N2 non-small-cell lung cancer (NSCLC) treated nationally with surgery and adjuvant chemotherapy. PATIENTS AND METHODS: Patients with pathologic N2 NSCLC who underwent complete resection and adjuvant chemotherapy from 2006 to 2010 were identified from the National Cancer Data Base and stratified by use of PORT (≥ 45 Gy). A total of 4,483 patients were identified (PORT, n = 1,850; no PORT, n = 2,633). The impact of patient and treatment variables on OS was explored using Cox regression. RESULTS: Median follow-up time was 22 months. On univariable analysis, improved OS correlated with younger age, treatment at an academic facility, female sex, urban population, higher income, lower Charlson comorbidity score, smaller tumor size, multiagent chemotherapy, resection with at least a lobectomy, and PORT. On multivariable analysis, improved OS remained independently predicted by younger age, female sex, urban population, lower Charlson score, smaller tumor size, multiagent chemotherapy, resection with at least a lobectomy, and PORT (hazard ratio, 0.886; 95% CI, 0.798 to 0.988). Use of PORT was associated with an increase in median and 5-year OS compared with no PORT (median OS, 45.2 v 40.7 months, respectively; 5-year OS, 39.3% [95% CI, 35.4% to 43.5%] v 34.8% [95% CI, 31.6% to 38.3%], respectively; P = .014). CONCLUSION: For patients with N2 NSCLC after complete resection and adjuvant chemotherapy, modern PORT seems to confer an additional OS advantage beyond that achieved with adjuvant chemotherapy alone.
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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 | 0.009 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.014 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".