Preoperative bowel preparation for patients undergoing elective colorectal surgery: a clinical practice guideline endorsed by the Canadian Society of Colon and Rectal Surgeons.
Bibliographic record
Abstract
BACKGROUND: Despite evidence that mechanical bowel preparation (MBP) does not reduce the rate of postoperative complications, many surgeons still use MBP before surgery. We sought to appraise and synthesize the available evidence regarding preoperative bowel preparation in patients undergoing elective colorectal surgery. METHODS: We searched MEDLINE, EMBASE and Cochrane Databases to identify randomized controlled trials (RCTs) comparing patients who received a bowel preparation with those who did not. Two authors reviewed the abstracts to identify articles for critical appraisal. We used the methods of the United States Preventive Services Task Force to grade study quality and level of evidence, as well as formulate the final recommendations. Outcomes assessed included postoperative infectious complications, such as anastomotic dehiscence and superficial surgical site infections. RESULTS: Our review identified 14 RCTs and 8 meta-analyses. Based on the quality and content of these original manuscripts, we formulated 6 recommendations for various aspects of bowel preparation in patients undergoing elective colorectal surgery. CONCLUSION: Taking into account the lack of difference in postoperative infectious complication rates when MBP is omitted and the adverse effects of MBP, we believe that, based on the literature, MBP before surgery should be omitted.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".