Long-Term Oncological Outcomes Related to Lymphadenectomy in Clinical Stage I NSCLC: A Multicenter Retrospective Experience
Bibliographic record
Abstract
BACKGROUND: Lymphadenectomy is considered a key part of the radical treatment of resectable lung cancer, although its appropriate extension in early stages is a debated topic due to the great heterogeneity of studies in the literature. This study aims to evaluate the impact of lymphadenectomy extent on survival and recurrence in the treatment of early-stage NSCLC patients undergoing lobectomy and lymph node dissection. METHODS: Data from clinical stage I NSCLC patients undergoing lobectomy and hilar-mediastinal lymphadenectomy at two thoracic surgery centers from 2016 to 2019 were retrospectively evaluated. Information regarding perioperative outcomes and lymphadenectomy details was collected and analyzed, and their impact on OS, CSS, and DFS was assessed. RESULTS: During the period under review, 323 patients with stage cI lung cancer underwent lobectomy with lymphadenectomy. Statistical analysis showed that the evaluated lymph nodal factors (mean number of lymph nodes removed and number and type of lymph node station explored) did not statistically significantly impact OS, CSS, and DFS at a median follow-up of 59 months (IQR 45-71). CONCLUSIONS: The results of this study suggest that a less invasive procedure than systematic lymphadenectomy could be performed in early-stage cases with adequate preoperative staging.
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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 | 0.000 |
| 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".