Does one-stitch method of temporary ileostomy affect the stoma-related complications after laparoscopic low anterior resection in rectal cancer patients?
Bibliographic record
Abstract
PURPOSE: This current study attempted to investigate whether one-stitch method (OM) of temporary ileostomy influenced the stoma-related complications after laparoscopic low anterior resection (LLAR). METHODS: We searched for eligible studies in four databases including PubMed, Embase, Cochrane Library, and CNKI from inception to July 20, 2023. Both surgical outcomes and stoma-related complications were compared between the OM group and the traditional method (TM) group. The Newcastle-Ottawa Scale (NOS) was adopted for quality assessment. RevMan 5.4 was conducted for data analyzing. RESULTS: Totally 590 patients from six studies were enrolled in this study (272 patients in the OM group and 318 patients in the TM group). No significant difference was found in baseline information (P > 0.05). Patients in the OM group had shorter operative time in both the primary LLAR surgery (MD = - 17.73, 95%CI = - 25.65 to - 9.80, P < 0.01) and the stoma reversal surgery (MD = - 18.70, 95%CI = - 22.48 to -14.92, P < 0.01) than patients in the TM group. There was no significant difference in intraoperative blood loss of the primary LLAR surgery (MD = - 2.92, 95%CI = - 7.15 to 1.32, P = 0.18). Moreover, patients in the OM group had fewer stoma-related complications than patients in the TM group (OR = 0.55, 95%CI = 0.38 to 0.79, P < 0.01). CONCLUSION: The OM group had shorter operation time in both the primary LLAR surgery and the stoma reversal surgery than the TM group. Moreover, the OM group had less stoma-related complications.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".