Robotic versus Open Surgery in Locally Advanced Non-Small Cell Lung Cancer: Evaluation of Surgical and Oncological Outcomes
Bibliographic record
Abstract
Locally advanced non-small cell lung cancer (NSCLC) consists of a heterogeneous group, with different pulmonary extension and lymph nodal involvement. Robotic surgery can play a key role in these tumours thanks to its technological features, although open surgery is still considered the gold-standard approach. Our study aims to evaluate the surgical and oncological outcomes of locally advanced NSCLC patients who underwent robotic surgery in a high-volume centre. Data from consecutive patients with locally advanced NSCLC who underwent robotic lobectomy were retrospectively analysed and compared with patients treated with open surgery. Clinical characteristics and surgical and oncological information were evaluated. From 2010 to 2020, 131 patients underwent anatomical lung resection for locally advanced NSCLC. A total of 61 patients were treated with robotic surgery (46.6%); the median hospitalization time was 5.9 days (range 2-27) and the postoperative complication rate was 18%. Open surgery was performed in 70 patients (53.4%); the median length of stay was 9 days (range 4-48) and the postoperative complication rate was 22.9%. The median follow-up time was 70 months. The 5-year overall survival was 34% in the robotic group and 31% in the thoracotomy group. Robotic surgery can be considered safe and feasible not only for early stages but also for the treatment of locally advanced NSCLC.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".