Laparoscopic surgery versus robot-assisted surgery for choledochal cyst excision: A systematic review and meta-analysis
Bibliographic record
Abstract
The aim of this following study is to systematically review and analyze the published data comparing laparoscopic surgery and robotic assisted surgery for choledochal cyst excisions through the metrics of operative time, length of hospital stay and postoperative outcome. PubMed, Web of Science, Embase, Ovid, and the Cochrane Library databases were combed through and data was retrieved from the timespan between January 1995 and October 2021. The primary measures included operative time, intraoperative bleeding, hospital stay, and postoperative complications. Quality and risk of bias were assessed using the Newcastle-Ottawa Quality Assessment Scale. Making use of random-effects models, we pooled the odds ratios (ORs) and mean differences (MDs) with 95% confidence intervals (95% CIs). Six studies comprising a total 484 patients who had undergone either laparoscopic surgery [307 (63.43%) patients] or robot-assisted surgery [177 (36.57%) patients] were included in this analysis. Three of the articles involved adults while the other three involved children. All of the studies were published after 2018 and were retrospective case–control studies. Patients undergoing robotic surgery had a shorter hospital stay (MD, 0.95; 95% CI, 0.56 to 1.35; p < 0.00001) and a longer operative time (MD, −57.52; 95% CI, −67.17 to −47.87; p < 0.00001). And there was no significant discrepancy in complications between the two groups. Compared to laparoscopic surgery, robot-assisted surgery is associated with a shorter hospital stay, scores highly in terms of both safety and feasibility, however it also results in a longer operative time. And the two procedures have the same short- and long-term results.
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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 | 0.014 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.031 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 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".