Comparative analysis of the safety and effectiveness of robotic natural orifice specimen extraction versus laparoscopic surgery for colorectal tumors through systematic review and meta-analysis
Bibliographic record
Abstract
The purpose of this study and meta-analysis was to evaluate the perioperative and oncologic results of robotic NOSE versus laparoscopic surgery for colorectal tumors. We plan to perform an extensive electronic search on PubMed, CNKI, Embase, and the Cochrane Library to find research articles published from the beginning of the databases until July 2024 that examine the comparison between robotic natural orifice specimen extraction and laparoscopic surgery in patients with colorectal cancer. Both English and Chinese literature will be included. Literature screening will strictly follow predetermined criteria for inclusion and exclusion, specifically targeting randomized controlled trials and cohort studies. The evaluation of quality will be conducted with the Newcastle-Ottawa Scale (NOS). Review Manager 5.4.1 will be utilized to perform a meta-analysis of data gathered from the studies that are included. The ultimate evaluation included seven past cohort studies with a total of 1117 participants (545 who had robotic NOSE and 572 who had laparoscopic surgery). Patients who had robotic NOSE experienced notable enhancements in LOHS, time to first flatus, time to start the liquid diet, EBL, and postoperative ileus when compared to patients undergoing laparoscopic colorectal surgery. There were no notable discrepancies noted in terms of surgical duration, total complications, lymph node collection, and anastomotic leakage between the two methods. In conclusion, the use of robotic technology for extracting specimens through natural body openings in colorectal surgery is considered to be safe and achievable. It offers notable advantages over laparoscopic surgery, including reduced hospital stay, earlier time to first flatus and liquid intake, decreased EBL, and lower incidence of postoperative ileus.
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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 | 0.025 | 0.049 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.048 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".