Video-Assisted Thoracic Surgery vs. Thoracotomy for the Treatment in Patients With Esophageal Leiomyoma: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: Surgical treatment is usually suitable for patients with esophageal leiomyoma. Video-assisted thoracic surgery (VATS) offers a minimally invasive approach to thoracotomy. However, there is no clear conclusion on whether VATS can achieve an equal or even better surgical effect when compared with the traditional open approach in the treatment of esophageal leiomyoma. We performed this meta-analysis to explore and compare the outcomes of VATS vs. thoracotomy for patients with esophageal leiomyoma. 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 were analyzed using the Review Manager 5.3 software. Fixed or random effect models were applied according to heterogeneity. Results: A total of 8 studies with 290 patients, of whom 141 patients were in the VATS group and 149 in the thoracotomy group, were involved in the analysis. Compared with thoracotomy, VATS was associated with shorter operative time, less blood loss in operation, and shorter postoperative hospital stay. There is no significant difference in postoperative pleural drainage day and postoperative complications between the two groups. Conclusions: VATS has more advantages over thoracotomy, indicating that VATS is better than thoracotomy in terms of postoperative recovery. We look forward to more large-sample, high-quality studies published in the future.
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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 | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.037 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".