Antibiotic prophylaxis for the prevention of surgical site infections following colorectal surgery: protocol for network meta-analysis of randomized trials
Bibliographic record
Abstract
BACKGROUND: Surgical site infections continue to be a significant challenge following colorectal surgery. These can result in extended hospital stays, hospital readmissions, increased treatment costs, and negative effects on patients' quality of life. Antibiotic prophylaxis plays a crucial role in preventing infection during surgery, specifically in preventing surgical site infections after colorectal surgery in adult patients. However, the optimal antibiotic regimen is still unclear based on current evidence. Considering the limitations of existing reviews, our goal is to conduct a comprehensive systematic review and network meta-analysis of randomized controlled trials to evaluate the comparative benefits and harms of available antibiotic prophylaxis regimens for preventing surgical site infections following colorectal surgery in adult patients. METHODS: We will search the Medline, EMBASE, CINAHL, Scopus, and Cochrane Central Register of Controlled Trials databases to identify relevant randomized controlled trials. We will include trials that (1) enrolled adults who underwent colorectal surgeries and (2) randomized them to any systemic administration of antibiotic (single or combined) prophylaxis before surgery compared to an alternative systemic antibiotic (single or combined antibiotic), placebo, control, or no prophylactic treatment. Pairs of reviewers will independently assess the risk of bias among eligible trials using a modified Cochrane risk of bias instrument for randomized trials. Our outcomes of interest include the rate of surgical site infection within 30 days of surgery, hospital length of stay, 30-day mortality, and treatment-related adverse effects. We will perform a contrast-based network meta-analysis using a frequentist random-effects model assuming a common heterogeneity parameter. The Grading of Recommendations Assessment, Development, and Evaluation (GRADE) approach will be utilized to assess the certainty of evidence for treatment effects. DISCUSSION: By synthesizing evidence from available RCTs, this study will provide valuable insight for clinicians, patients, and health policymakers on the most effective antibiotics for preventing surgical site infection. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42023434544.
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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 | 0.091 | 0.189 |
| Meta-epidemiology (narrow) | 0.008 | 0.004 |
| Meta-epidemiology (broad) | 0.019 | 0.032 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.050 | 0.004 |
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".