Antibiotic Prophylaxis against Surgical Site Infection after Open Hernia Surgery: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
OBJECTIVE: The role of antibiotic prophylaxis (AP) in the prevention of surgical site infection (SSI) after hernia repair is debated. We conducted this systematic review and meta-analysis to assess the evidence on the value of prophylactic antibiotics in reducing the risks of SSI after open hernia surgery. METHODS: We ran an online and manual search to identify relevant randomized controlled trials that compared prophylactic antibiotics to nonantibiotic controls in patients undergoing open surgical hernia repair. Data on SSI risk were extracted and pooled as risk ratios (RRs) with 95% confidence intervals (95% CIs), using RevMan software. We further used the Cochrane risk of bias tool and GRADE assessment to evaluate the quality of generated evidence. RESULTS: Twenty-nine studies (N = 8,616 patients) were included in the current analysis. Antibiotic prophylaxis reduced the risk of SSI in open hernia repair patients (RR = 0.65, 95% CI = 0.53, 0.79). Subgroup analysis showed a significant benefit for antibiotics in mesh repair patients (RR = 0.60, 95% CI = 0.48, 0.76) yet no significant difference in SSI risk after herniorrhaphy (RR = 0.86, 95% CI = 0.54, 1.36). In addition, AP was associated with a significant reduction in superficial SSI risk (RR = 0.56, 95% CI = 0.43, 0.72) but not deep SSI (RR = 0.70, 95% CI = 0.30, 1.62). Further analysis showed a significant reduction in SSI risk with amoxicillin/clavulanic acid and cefazolin but not with cefuroxime. CONCLUSION: The present meta-analysis suggests that AP is beneficial prior to open mesh hernia repair. However, the quality of evidence was low, and further well-designed trials are needed.
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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 | 0.015 | 0.040 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.037 |
| Bibliometrics | 0.007 | 0.007 |
| 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".