REVIEW OF DECOMPRESSION ILEOSTOMY DURING LAPAROSCOPIC RECTAL CANCER SURGERY
Bibliographic record
Abstract
Introduction: Decompression ileostomy is always applied to avoid clinically signicant anastomotic leakage and other postoperative complications for patients receiving laparoscopic rectal cancer surgery. However, whether it is necessary to perform the ileostomy is still controversial. This analysis aims to analyse the efcacy of ileostomy on laparoscopic rectal cancer surgery. Methods: Cochrane Library, EMBASE, Web of Science, and PubMed were applied for systematic search of all relevant literature, updated to March 31, 2022. Studies compared patients with and without ileostomy for laparoscopic rectal cancer surgery. The analysis was performed using Review Manager software. The quality of the non-randomized controlled trials was assessed using the Newcastle-Ottawa scale (NOS), and the randomized studies were assessed using the Jadad scale. Results: A total of 1203 references were obtained, and 7 studies were included using the research methods. The clinically signicant anastomotic leakage rate was signicantly lower in ileostomy group (27/567, 4.76%) than that in non-ileostomy group (54/525, 10.29%) (RR = 0.47, 95% CI 0.30–0.73, P for overall effect = 0.0009, P for heterogeneity = 0.18, I2 = 32%). However, the postoperative hospital stay, reoperation, wound infection, and operation time showed no signicant difference between the ileostomy and non-ileostomy groups. Conclusion: The results demonstrated that protective ileostomy can decrease the clinically signicant anastomotic leakage rate for patients undergoing laparoscopic rectal cancer surgery. However, ileostomy has no effect on postoperative hospital stay, reoperation, wound infection, and operation time
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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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.005 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".