Robotic-assisted versus video-assisted thoracoscopic surgery for thymic epithelial tumours, from the European Society of Thoracic Surgeons Database
Bibliographic record
Abstract
OBJECTIVES: Minimally invasive thymectomy is an accepted approach for early-stage thymic epithelial neoplasia, reducing pain and length of stay compared with open surgery. In this study, we compare robotic and video-assisted thymectomy to assess pathological resection status, overall and disease-free survival. METHODS: Data were retrieved from the European Society of Thoracic Surgeons prospectively maintained thymic database. Eighty-two international centres were invited to participate in the ESTS registry. Thirty-seven centres agreed to take part. We included all patients who had undergone complete thymectomy for malignancy through a minimally invasive approach and excluded patients in whom complete data were not available. RESULTS: Between October 2001 and May 2021, a total of 899 patients with thymic malignancy underwent minimal access surgical resection and were included in the study. A propensity matched analysis was conducted with interrogation of 732 patients. Median age was 55 years, and 408 (56%) patients were female. Propensity matched was performed with 1:1 matching for surgical approach (video assisted = 366, robot assisted = 366). Robot-assisted surgery conferred significantly lower odds of incomplete resection (R1; 0.203 95% CI 0.13-0.317; P < 0.001). However, there was no difference in terms of overall and disease-free survival between the 2 techniques. CONCLUSIONS: In this analysis, after adjusting for thymoma stage, the odds of incomplete surgical resection were higher in patients undergoing video-assisted surgery than robotic. However, there was no difference in overall or disease-free survival. With data maturation and increased follow-up, this would need repeat analysis and perhaps may provide more credence to the concept of a prospective randomized study to compare outcomes in thymic epithelial neoplasia by surgical approach with a standardized pathological work-up.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.023 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".