Robotic-assisted minimally invasive, open, and conventional minimally invasive esophagectomy for esophageal carcinoma – a comprehensive quantitative analysis of perioperative outcomes and long-term survival
Bibliographic record
Abstract
BACKGROUND: Robotic-assisted minimally invasive esophagectomy (RAMIE) has emerged as a promising alternative for esophageal carcinoma (EC). This quantitative analysis evaluates perioperative and long-term outcomes of RAMIE versus open esophagectomy (OE) and minimally invasive esophagectomy (MIE). METHODS: Following PRISMA guidelines, databases including PubMed, Web of Science, Cochrane Library, and Embase were queried for studies on perioperative and long-term outcomes of RAMIE, OE, and MIE for EC up to 1 June 2024. Data extraction and quality assessment were performed using the Newcastle-Ottawa Scale (NOS) and the Cochrane Collaboration's Risk of Bias Tool. Fixed-effects or random-effects models pooled ORs, HRs, MD, and their 95% CI based on heterogeneity. RESULTS: Fifty studies comprising 10,127 patients were included. Compared to OE, RAMIE significantly reduced pulmonary complications, gastric retention, cardiovascular complications, and wound infections, despite longer operative times. Compared to MIE, RAMIE had more abdominal and left recurrent laryngeal nerve lymph node dissections, lower rates of postoperative pulmonary complications, anastomotic leakage, gastric retention, and recurrent laryngeal nerve injury, but longer operative times. RAMIE patients had shorter ICU stays than the both traditional surgical methods. There were no significant differences in long-term overall survival and disease-free survival between RAMIE, OE, and MIE. CONCLUSIONS: RAMIE shows favorable perioperative outcomes and reduced postoperative complications compared to OE and MIE, with comparable long-term survival. Despite longer operative times, RAMIE is effective for EC patients. Future research should focus on optimizing surgical time and conducting randomized controlled trials to confirm its benefits, guiding clinical decision-making and optimizing patient care.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".