[A meta-analysis on risk factors of postoperative perineal wound complications after abdominoperineal resection for rectal cancer].
Bibliographic record
Abstract
OBJECTIVE: To investigate the risk factors of postoperative perineal wound complications after abdominoperineal resection for rectal cancer. METHODS: The databases of Medline, Embase, Web of Science, Ovid, Cochrane Library, CBM, CNKI, VIP and WANFANG were searched for the studies of abdominoperineal resection up to October 2016. The quality of the included studies was assessed by using "Cochrane collaboration's tool for assessing risk of bias" and "the Newcastle-Ottawa Scale". The meta-analyses were performed with Review Manager 4.3 software. RESULTS: Eight randomized controlled trials and 33 non-randomized controlled trials with 15 287 patients were enrolled. Meta-analyses showed that neoadjuvant radiotherapy (OR=2.55, 95%CI: 1.66 to 3.93, P<0.01) and obesity (OR=2.12, 95%CI: 1.05 to 4.26, P=0.04) significantly increased the morbidity of perineal wound complication after abdominoperineal resection for rectal cancer; omentoplasty(OR=0.30, 95%CI: 0.14 to 0.67, P=0.003), presacral space clysis (OR=0.11, 95%CI: 0.01 to 0.94, P=0.04), abdominal drainage (OR=0.36, 95%CI: 0.21 to 0.63, P<0.01), perineal skin drainage(OR=41.72, 95%CI: 2.39 to 727.90, P=0.01) and local application of antibiotics (OR=0.17,95%CI: 0.07 to 0.40, P<0.01) significantly decreased the morbidity of perineal wound complication; however, extralevator abdominoperineal excision (OR=0.88, 95%CI: 0.57 to 1.35, P=0.56), laparoscopic procedure (OR=1.02, 95%CI: 0.47 to 2.21, P=0.96), biologic mesh reconstruction (OR=1.81, 95%CI: 0.95 to 3.46, P=0.07), myocutaneous flap reconstruction (OR=1.32, 95%CI: 0.18 to 9.91, P=0.79) and negative pressure drainage(OR=0.69, 95%CI: 0.35 to 1.34, P=0.27) had no influence on the healing of perineal wound. CONCLUSIONS: Numerous factors can affect the occurrence of perineal wound complication after abdominoperineal resection for rectal cancer. Due to the limitations of enrolled studies, multicenter large scale and high-quality randomized controlled trials are required to validate the current 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 | 0.009 | 0.021 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.050 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".