The extent of mediastinal lymph node dissection correlates with survival of small cell lung cancer patients after resection: a propensity score-matched cohort study analysis
Bibliographic record
Abstract
Background: Evidence on the importance of lymph node (LN) dissection during resection for small cell lung cancer (SCLC) is scarce. This study sought to investigate the clinical impact of the extent of lymphadenectomy on the survival of patients with SCLC. Methods: Patients who underwent resection for primary SCLC between 2000 and 2016 were identified from the Surveillance, Epidemiology, and End Results (SEER) cancer registry. The patients were stratified based on the number of LNs dissected (0, 1-3, 4-11, and ≥12) via an X-Tile software analysis, and lung cancer-specific survival (LCSS) and overall survival (OS) were compared between these stratified groups using Kaplan-Meier curves. A propensity score-matched analysis and a Cox regression model were used to adjust for potential confounders. Results: A total of 1,883 patients with SCLC met our criteria and were enrolled in the study. The LCSS and OS analyses revealed that patients who underwent LN dissection during surgery had longer survival times significantly than patients who did not. Similarly, patients who underwent more extensive LN dissection (≥4 LNs) had longer survival times than those who underwent less extensive LN dissection (1-3 LNs). However, no significant increase in survival time was found for patients who underwent the dissection of ≥12 LNs compared to those who underwent the dissection of 4-11 LNs. These results were confirmed in our propensity-matched and Cox regression analyses. Conclusions: Our study revealed that patient survival after surgical resection for SCLC is associated with the number of dissected LNs, and the number of LNs for dissection ranges from 4 to 11 achieve the best survival outcome.
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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 | 0.002 |
| 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 | 0.000 |
| 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".