Postoperative antibiotic prophylaxis in total hip and knee arthroplasty: a systematic review and meta-analysis of randomized controlled trials
Bibliographic record
Abstract
BACKGROUND: Postoperative antibiotic prophylaxis is currently the standard of care for patients undergoing total hip and knee arthroplasty. We evaluated the evidence for this practice in the reduction of surgical-site infections. METHODS: We systematically searched MEDLINE, Embase and the Cochrane Library for randomized controlled trials (RCTs) published up to Aug. 15, 2014. We included all RCTs that compared postoperative antibiotic prophylaxis with postoperative placebo or no treatment in patients undergoing primary total hip or knee arthroplasty for osteoarthritis. We combined outcomes for surgical-site infection using a random-effects model and quantified heterogeneity using the χ2 test and the I2 statistic. We assessed the overall quality of the evidence according to the Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach. RESULTS: We identified 4 RCTs (n = 4036) that met the inclusion criteria. Surgical-site infections occurred in 3.1% (63/2055) of patients in the prophylaxis group and 2.3% (45/1981) in the control group. Postoperative prophylaxis did not reduce the rate of surgical-site infections compared with placebo (risk difference 0.01, 95% confidence interval 0.00 to 0.02; I2 = 26%). This result was robust to sensitivity testing for losses to follow-up. According to the GRADE approach, the overall quality of evidence was very low. INTERPRETATION: The available evidence did not show efficacy of postoperative antibiotic prophylaxis for the prevention of surgical-site infections in patients undergoing total hip or knee arthroplasty. Multicentred RCTs are likely to have an important impact on the confidence in the effect estimate and to change the estimate itself.
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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 | 0.023 | 0.062 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.025 | 0.034 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".