Clinical efficacy and safety of robot assisted surgery for choledochal cysts excisions: a systematic review and meta-analysis
Bibliographic record
Abstract
This study aimed to evaluate the safety and therapeutic effect of Robot-assisted surgery (RAS) for choledochal cysts (CCs) excisions. PubMed, EMBASE, Cochrane Library, Web of Science, CNKI, WanFang, VIP, and CBM were searched from database inception to 1 May 2022. The Newcastle-Ottawa scale (NOS) was used to conduct quality assessments, and RevMan (Version 5.4) was used to perform the meta-analysis. In all, 9 studies, involving 623 patients, were analyzed. RAS compared with LAS was associated with less intraoperative blood loss, shorter time to start solid diets, shorter postoperative hospital stay, and lower complications. There was no significant difference in operative time between the two groups, but the total costs were higher in RAS. Our subgroup analysis showed that RAS had significant advantages over LAS in the child group: minor bleeding, shorter length of hospital stay, and fewer postoperative complications. The available evidence indicates that the RAS system has the advantages of less intraoperative blood loss, minor tissue damage, quick recovery, and sound healing in treating choledochal cyst, which proves that the RAS is safely feasible. Especially in children, RAS tends to be a better choice.
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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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.020 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".