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 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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".