Effectiveness of Prophylactic Antibiotic Administration in Preventing Surgical Site Infections in Abdominal Surgery – A Meta-Analysis of RCTs & Observational Studies
Bibliographic record
Abstract
Background: Surgical site infections (SSIs) are a major postoperative complication in abdominal surgeries, contributing to increased morbidity, prolonged hospitalization, and healthcare costs. Prophylactic antibiotic administration has been widely recommended, yet clinical practices vary significantly, especially across different healthcare settings. Objective: This meta-analysis aims to evaluate the effectiveness of prophylactic antibiotics in preventing SSIs among patients undergoing abdominal surgeries, with a focus on timing (pre-operative vs. post-operative), study design, and country income classification. Methods: A systematic search of PubMed, Scopus, Web of Science, and Cochrane CENTRAL was conducted up to March 2024, including randomized controlled trials (RCTs) and observational studies. Studies reporting on antibiotic prophylaxis and SSIs in abdominal surgeries were included. Risk ratios (RRs) with 95% confidence intervals (Cis) were calculated using a random-effects model. Subgroup analyses were performed based on antibiotic timing, study design, and country income level. Risk of bias was assessed using the Cochrane RoB 2.0 and Newcastle-Ottawa Scale. Results: Three studies (n = 9,790) met inclusion criteria. Overall, prophylactic antibiotics were associated with a 30% relative reduction in SSI risk (RR = 0.70; 95% CI: 0.38–1.30), though not statistically significant (P = 0.26), with high heterogeneity (I² = 85%). Subgroup analysis revealed significant benefit in RCTs (RR = 0.54; 95% CI: 0.38–0.77; P = 0.0006) and with pre-operative administration (RR = 0.54; 95% CI: 0.38–0.77; P = 0.0006), while post-operative use showed no benefit (RR = 1.04; 95% CI: 0.93–1.16; P = 0.48). Conclusion: Prophylactic antibiotics, especially when administered pre-operatively, are effective in reducing SSIs following abdominal surgery. Timing and study design significantly influence outcomes. These findings support current global guidelines and emphasize the need for standardized practices, particularly in low-resource settings. Further high-quality RCTs are recommended to enhance generalizability and inform global surgical protocols.
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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 | 0.034 | 0.079 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.067 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| 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".