The outcomes of robotic ileocolic resection in Crohn’s disease compared with laparoscopic and open surgery: a meta-analysis and systematic review
Bibliographic record
Abstract
BACKGROUND: This is the first review providing insights into the outcomes of robotic ileocolic resection for Crohn's disease, potentially guiding improved surgical decisions and patient outcomes and comparing outcomes with laparoscopic and open approaches. METHODS: The review was registered prospectively with PROSPERO (CRD42024504839). A comprehensive search of MEDLINE, Embase, Scopus, and Cochrane Central databases for studies on robotic ileocolic resection for Crohn's disease from inception to February 2024 was conducted. Eligible studies included participants over 18 years of age with Crohn's disease undergoing robotic ileocolic resection. Data were extracted according to PRISMA guidelines. For single-arm analyses, the random-effects model was used, while two-arm analyses employed the inverse variance and Mantel-Haenszel methods. RESULTS: The analysis included eight studies with 5760 patients, among whom 369 underwent robotic ileocolic resection. The mean operative time for robotic procedures was 226 min. Postoperative complications included ileus in 12.50% and wound complications in 7.00%, while reoperations and readmissions occurred in 3.60% and 13.20% of patients, respectively. When compared with laparoscopic procedures, robotic procedures showed shorter length of hospital stay and longer operative times but similar total complication, reoperation, and conversion rates. In contrast, robotic procedures had fewer total postoperative complications compared with open surgeries, despite longer operative times. CONCLUSIONS: Robotic ileocolic resection for Crohn's disease, while having a longer operative time, results in fewer postoperative complications compared with open surgery and shows comparable outcomes to laparoscopic procedures with shorter hospital stays.
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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 | 0.017 | 0.046 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.041 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".