The Changing Landscape of Lung Cancer Resection Outcomes Over the Past two Decades
Bibliographic record
Abstract
INTRODUCTION: Recent advances in cancer management may have transformed the overall prognosis of patients undergoing lung cancer resection. This study aimed to assess the changes in the long-term survival of patients undergoing surgery for lung cancer over the last 2 decades and to identify the risk factors modulating the postoperative prognosis. METHODS: This single-center retrospective study included nonsmall cell lung cancer patients who underwent lung resection between 2008 and 2020. Exclusion criteria included prior lung cancer or resection, diagnosis of lung metastases, small cell lung cancers, carcinoid tumors, or benign tumors. Multivariate models analyzed determinants of short- and long-term outcomes over time. RESULTS: Among 2898 lung resections performed, 768 deaths (26.5%) occurred, including 25 (0.9%) in the 30-day postoperative period. Postoperative complications were observed in 1063 cases (36.7%), with 535 (18.5%) being respiratory-related. No significant improvement was observed in 30-day postoperative complications or deaths over time. Conversely, the adjusted hazard ratio (HR) for mortality was 0.72 (95% CI, 0.57-0.92) and 0.59 (95% CI, 0.45-0.78) for the in 2012-2015 and 2016-2020 periods compared to 2008-2011. In multivariate analyses, increasing age, current smoking at the time of surgery, pneumonectomy, advanced cancer stage and lower socioeconomic status were associated with worse outcomes. CONCLUSIONS: Over the past 2 decades, long-term survival after lung cancer resection has significantly improved, despite stable rates of early postoperative complications. Efforts to develop curative treatments for advanced stages remain crucial while public health initiatives must address socioeconomic disparities to further improve lung cancer outcomes.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".