Meta-analysis of the effectiveness of prophylactic antibiotics in the prevention of postoperative complications after tension-free hernioplasty
Bibliographic record
Abstract
BACKGROUND: Previous reviews of the effectiveness of antibiotic prophylaxis for elective inguinal hernia repair were not conclusive owing to the limited number of patients enrolled in randomized controlled trials (RCTs). However, since new RCTs involving patients undergoing tention-free hernioplasty have been published in recent years, we performed a new meta-analysis to evaluate the effectiveness of antibiotic prophylaxisin the prevention of postoperative complications after this procedure. METHODS: We performed a meta-analysis of RCTs studying the use of antibiotic prophylaxis to prevent postoperative complications in patients undergoing tension-free hernioplasty. RESULTS: We included 6 RCTs conducted around the world in our analysis. Compared with the control condition, antibiotic prophylaxis was associated with a lower incidence of incision infection (odds ratio [OR] 0.45, 95% confidence interval [CI] 0.26-0.77, p = 0.004). There were no significant differences in risk for incision hematoma (OR 1.57, 95% CI 0.60-4.10, p = 0.35), respiratory infection (OR 1.00, 95% CI 0.17-5.79, p > 0.99) or urinary tract infection (OR 1.81, 95% CI 0.38-8.52, p = 0.45) between the 2 conditions. CONCLUSION: Antibiotic prophylaxis use in patients undergoing tension-free hernioplasty decreases the rate of incision infection by 55%.
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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 | 0.016 | 0.044 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.038 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| 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".