Preoperative risk factors for anastomotic leakage after resection for colorectal cancer: a systematic review and meta‐analysis
Bibliographic record
Abstract
AIM: Colorectal anastomotic leakage is a serious complication. Despite extensive research, no consensus on the most important preoperative risk factors exists. The aim of this systematic review and meta-analysis was to evaluate risk factors for anastomotic leakage in patients operated with colorectal resection. METHOD: The databases MEDLINE, Embase and CINAHL were searched for prospective observational studies on preoperative risk factors for anastomotic leakage. Meta-analyses were performed on outcomes based on odds ratios (OR) from multivariate regression analyses. The Newcastle-Ottawa scale was used for bias assessment within studies, and the Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach was used for quality assessment of evidence on outcome levels. RESULTS: This review included 23 studies evaluating 110,272 patients undergoing colorectal resection for cancer. The meta-analyses found that a low rectal anastomosis [OR = 3.26 (95% CI: 2.31-4.62)], male gender [OR = 1.48 (95% CI: 1.37-1.60)] and preoperative radiotherapy [OR = 1.65 (95% CI: 1.06-2.56)] may be risk factors for anastomotic leakage. Primarily as a result of observational design, the quality of evidence was regarded as moderate or low for these risk factors according to the GRADE approach. CONCLUSION: Based on the best available evidence, important preoperative risk factors for colorectal anastomotic leakage have been identified. Knowledge on risk factors may influence treatment and procedure-related decisions, and possibly reduce the leakage rate.
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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 | 0.010 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.030 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| 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".