Outside the Cage Subcostal RATS Lobectomy: Technical Aspects and Results of the First Series of a Novel Approach to Pulmonary Lobectomy
Bibliographic record
Abstract
OBJECTIVE: The goal of minimally invasive surgery is to reduce trauma to patients and improve their postoperative outcomes. In this context, the utilization of robot-assisted thoracic surgery (RATS) in the treatment of lung cancer has increased worldwide. The feasibility of single-incision major pulmonary resections by RATS was recently reported, with the objective of minimizing the surgical trauma of the traditional multiportal RATS approach. However, both techniques require intercostal incisions, potentially causing immediate and chronic pain resulting from intercostal nerve injury. To reduce postoperative pain resulting from intercostal approaches, we developed a nonintercostal, outside the thoracic cage (OTC) approach for RATS lobectomy, avoiding intercostal instrumentation. This report aims to describe the results of the first reported series of OTC subcostal RATS lobectomies. METHODS: Retrospective analysis of a series of the first consecutive patients operated on using the novel OTC subcostal RATS lobectomy technique. RESULTS: , and the median American Society of Anesthesiologists score was III (II to IV). No serious adverse events were observed, and there was no conversion of the surgical technique. The mean operative time was 132.6 (98 to 223) min. The median length of stay was 2 days. No pain-related complications, readmissions, or 30-day mortality were observed. CONCLUSIONS: This series demonstrates that OTC RATS lobectomy is feasible and safe. A phase I clinical trial is currently underway to prospectively assess the safety of the technique as well as its clinical relevance.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".