The Population-based Impact of Adjuvant Chemotherapy on Outcomes in T2N0M0 Non–Small Cell Lung Cancer
Bibliographic record
Abstract
OBJECTIVES: The value of adjuvant chemotherapy in T2N0M0 non-small-cell lung cancer (NSCLC) is unclear. Some current guidelines suggest adjuvant chemotherapy be considered for patients with tumors ≥4 cm. Prior population-based evaluations lacked lung cancer-specific survival (LCSS) and health insurance status. The authors aimed to identify predictors of adjuvant chemotherapy use and assess its real-world benefit in T2N0M0 NSCLC. MATERIALS AND METHODS: The authors included patients who underwent surgery for T2N0M0 NSCLC in a large Canadian province with universal health care between 2004 and 2015, grouping cases by adjuvant chemotherapy receipt. They identified predictors of chemotherapy use with logistic regression and correlates of overall survival (OS) and LCSS using Cox regression. RESULTS: The authors analyzed 967 patients. The median age was 68 years (interquartile range, 61 to 74), 455 (47%) were men, and 164 (17%) received adjuvant chemotherapy. Sex, tumor location, and laterality were similar between groups. Younger age, lower Charlson comorbidity score, large cell histology, and tumor size ≥4 cm were associated with a higher likelihood of chemotherapy receipt (all P<0.05). In the entire cohort and in the ≥4 and ≥5 cm subgroups, chemotherapy improved OS but not LCSS on univariate analysis. Chemotherapy was not associated with OS or LCSS in multivariate analysis (OS hazard ratio [HR], 0.925; 95% confidence interval [0.693-1.236], P=0.598, 0.725 [0.454-1.157], P=0.177 in the ≥4 cm group; LCSS HR, 1.196 [0.843-1.695], P=0.316, 0.917 [0.533-1.577], P=0.754 in the ≥4 cm group). CONCLUSION: Adjuvant chemotherapy was not associated with improved survival in this population-based T2N0M0 NSCLC cohort, even for ≥4 or ≥5 cm tumors, suggesting that it has a limited role in real-world practice.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".