Preoperative risk factors for anastomotic leakage after resection for colorectal cancer: a systematic review and meta‐analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.007 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it