Robot‐assisted thoracic surgery versus video‐assisted thoracic surgery for treatment of patients with thymoma: A systematic review and meta‐analysis
Bibliographic record
Abstract
BACKGROUND: Surgical resection of the thymus is indicated in the presence of primary thymic diseases such as thymoma. Video-assisted thoracoscopic surgery (VATS) and robot-assisted thoracic surgery (RATS) offer a minimally invasive approach to thymectomy. However, there is no clear conclusion whether RATS can achieve an equal or even better surgical effect when compared with VATS in treatment of thymoma. We performed this meta-analysis to explore and compare the outcomes of RATS versus VATS for thymectomy in patients with thymoma. METHODS: PubMed, Cochrane Library, EMBASE, China National Knowledge Infrastructure (CNKI), Medline, and Web of Science databases were searched for full-text literature citations. The quality of the articles was evaluated using the Newcastle-Ottawa Scale and the data analyzed using Review Manager 5.3 software. Fixed or random effect models were applied according to heterogeneity. Subgroup analysis was conducted. RESULTS: A total of 11 studies with 1418 patients, of whom 688 patients were in the RATS group and 730 in the VATS group, were involved in the analysis. Compared with VATS, RATS was associated with less blood loss in operation, lower volume of drainage, fewer postoperative pleural drainage days, shorter postoperative hospital stay, and fewer postoperative complications. There was no significant difference in operative time and patients with or without myasthenia gravis between the two groups. CONCLUSIONS: RATS has more advantages over VATS, indicating that RATS is better than VATS in terms of postoperative recovery. We look forward to more large-sample, high-quality randomized controlled studies published in the future.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.028 | 0.008 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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; both teacher heads agree on what is shown here.
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".