Robotic-assisted thoracoscopic surgery for lung resection: the first Canadian series
Bibliographic record
Abstract
Background: Robotic surgery was introduced as a platform for minimally invasive lung resection in Canada in October 2011. We present the first Canadian series of robotic pulmonary resection for lung cancer. Methods: Prospective databases at 2 institutions were queried for patients who underwent robotic resection for lung cancer between October 2011 and June 2015. To examine the effect of learning curves on patient and process outcomes, data were organized into 3 temporal tertiles, stratified by surgeon. Results: A total of 167 consecutive patients were included in the study. Median age was 66 (range 27–88) years, and 46.1% (n = 77) of patients were men. The majority of patients (n = 141, 84%) underwent robotic lobectomy. Median duration of surgery was 270 (interquartile range [IQR] 233–326) minutes, and median length of stay (LOS) was 4 (IQR 3–6) days. Twelve patients (7%) were converted to thoracotomy. Total duration of surgery and console time decreased significantly (p < 0.001) across tertiles, with a steady decline until case 20, followed by a plateau effect. Across tertiles, there was no significant difference in LOS, number of lymph node stations removed, or perioperative complications. Conclusion: The results of this case series are comparable to those reported in the literature. A prospective study to examine the outcomes and cost of robotic pulmonary resection compared with video-assisted thoracoscopic surgery should be done in the context of the Canadian health care system. We have presented the first consecutive case series of robotic lobectomy in Canada. Outcomes compare favourably to other series in the literature.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".