Comparison of Robotic and Open Lobectomy for Lung Cancer in Marginal Pulmonary Function Patients: A Single-Centre Retrospective Study
Bibliographic record
Abstract
Background: The treatment of non-small-cell lung cancer (NSCLC) patients with reduced respiratory function represents a challenge for thoracic surgeons. Minimally invasive surgery seems to be beneficial for these patients because it reduces tissue trauma and its impact on respiratory mechanics. Application of the robotic technique, the use of CO2 insufflation and longer surgical time are factors that could influence the outcomes of marginal pulmonary function patients. The objective of this study was to evaluate the impact of the robotic technique on the postoperative outcomes of patients with poor lung function. Methods: We retrospectively collected and analyzed data from consecutive marginal respiratory function patients who underwent robotic or open lobectomy for NSCLC. Data regarding clinical, operative and postoperative details were compared between the open and robotic approaches. Results: The outcomes of 100 patients with reduced respiratory function were evaluated, of whom 59 underwent open lobectomies and 41 underwent robotic lobectomies. Robotic lobectomy was characterized by a longer operative time, a reduced hospital stay and a lower incidence of postoperative complications (22% vs. 33.9%), when compared to the open approach. Conclusion: Robotic lobectomy is a safe and feasible procedure for patients with marginal pulmonary function.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".