Effect of Local Antibiotic Prophylaxis on Postoperative Deep Infection in Fracture Surgery: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
OBJECTIVES: Despite the use of systemic antibiotic prophylaxis, postoperative infection after fracture surgery remains an issue. The purpose of this systematic review and meta-analysis was to evaluate the effect of locally applied antibiotics on deep infection in fracture surgery in both the open and closed fractures. DATA SOURCES: A comprehensive search of MEDLINE, Embase, and PubMed was performed from the date of inception to April 15, 2021, and included studies in all languages. STUDY SELECTION: Cohort studies were eligible if they investigated the effect on the infection rate of local antibiotic prophylaxis on deep infection after fracture surgery. DATA EXTRACTION: This study was conducted according to the Cochrane Handbook for Systematic Reviews and reported as per the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Risk of bias was assessed using version 2 of the Cochrane risk-of-bias tool for randomized trials and the Methodological Index for Nonrandomized Studies tool where applicable. DATA SYNTHESIS: An inverse variance random-effects model was the primary analysis model because of the anticipated diversity in the evaluated populations. Univariate models were used when a single outcome was of interest. CONCLUSIONS: The risk of deep infection was significantly reduced when local antibiotics were applied compared with the control group receiving systemic prophylaxis only. This beneficial effect was observed in open fractures but failed to reach statistical significance in closed fractures. This meta-analysis suggests that there may be a significant risk reduction in deep infection rate after fracture surgery when local antibiotics are added to standard systemic prophylaxis, particularly in open fractures. Further high-powered Level I studies are needed to support these findings. LEVEL OF EVIDENCE: Therapeutic Level III. See Instructions for Authors for a complete description of levels of evidence.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.015 | 0.007 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".