Uniportal video-assisted thoracoscopic surgery: safety, efficacy and learning curve during the first 250 cases in Quebec, Canada
Bibliographic record
Abstract
BACKGROUND: Video-assisted thoracoscopic surgery (VATS) using a single incision (uniportal) may result in better pain control, earlier mobilization and shorter hospital stays. Here, we review the safety and efficiency of our initial experience with uniportal VATS and evaluate our learning curve. METHODS: We conducted a retrospective review of uniportal VATS using a prospectively maintained departmental database and analyzed patients who had undergone a lung anatomic resection separately from patients who underwent other resections. To assess the learning curve, we compared the first 10 months of the study period with the second 10 months. RESULTS: From January 2014 to August 2015, 250 patients underwent intended uniportal VATS, including 180 lung anatomic resections (72%) and 70 other resections (28%). Lung anatomic resection was successfully completed using uniportal VATS in 153 patients (85%), which comprised all the anatomic segmentectomies (29 patients), 80% (4 of 5) of the pneumonectomies and 82% (120 of 146) of the lobectomies attempted. The majority of lung anatomic resections that required conversion to thoracotomy occurred in the first half of our study period. Seventy patients underwent other uniportal VATS resections. Wedge resections were the most common of these procedures (25 patients, 35.7%). Although 24 of the 70 patients (34%) required the placement of additional ports, none required conversion to thoracotomy. CONCLUSIONS: Uniportal VATS was safe and feasible for both standard and complex pulmonary resections. However, when used for pulmonary anatomic resections, uniportal VATS entails a steep learning curve.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".