342. ROBOTIC-ASSISTED MINIMALLY INVASIVE, OPEN, AND CONVENTIONAL MINIMALLY INVASIVE ESOPHAGECTOMY FOR ESOPHAGEAL CARCINOMA—COMPREHENSIVE QUANTITATIVE ANALYSIS OF PERIOPERATIVE OUTCOMES AND SURVIVAL
Bibliographic record
Abstract
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 June 1, 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. Conclusion 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 safe and feasible 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 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 | 0.023 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.017 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".