Single- Versus Multiple-Dose Antibiotic Prophylaxis in the Surgical Treatment of Closed Fractures: A Meta-Analysis
Bibliographic record
Abstract
OBJECTIVES: The use of prophylactic antibiotics in the surgical treatment of closed long bone fractures is well established. The duration and dosage of prophylaxis, however, vary significantly among surgeons. A systematic review and meta-analysis were performed to determine if multiple-dose perioperative antibiotic prophylaxis is more effective than a single preoperative dose in the prevention of surgical wound infections during the treatment of closed long bone fractures. DATA SOURCES: Articles were identified by searching the following medical databases: Medline, Medline In Process & Other Non-indexed Citations, Embase, CENTRAL, and the Cochrane Database of Systematic Reviews. Relevant conference proceedings and the reference section of selected manuscripts were also searched for additional studies. STUDY SELECTION: Studies were included if they were prospective randomized controlled trials of patients with closed fractures treated with surgical fixation or arthroplasty. The interventions must have directly compared a single preoperative prophylactic dose to a multiple-dose perioperative strategy. Studies were excluded if they involved open fractures. DATA EXTRACTION: The demographic information, prophylaxis strategy, wound infection rate, and risk ratio were extracted from each article. DATA SYNTHESIS: Seven trials and 3,808 patients were pooled using a random effects model. When compared to a regimen of multiple doses of prophylactic antibiotics, administration of a single preoperative dose has a risk ratio of 1.24 (95% CI 0.60-2.60). The pooled risk difference between the 2 strategies is 0.005 (95% CI -0.011-0.021). Neither result is significant. CONCLUSIONS: In the setting of closed long bone fractures, the pooled results failed to demonstrate superiority of multiple-dose prophylaxis over a single-dose strategy. The pooled estimates suggest that surgical wound infections are relatively rare events and that any potential difference in infection rates between prophylaxis strategies is likely quite small. However, because the confidence interval surrounding the pooled risk ratio spans 1.0 by such a large amount, we are unable to definitively recommend a preferred dosing regimen to prevent surgical wound infections. Although future research is required to ensure our prophylaxis decisions continue to be evidence based and cost-effective, it is unlikely that a single clinical trial will be able to provide the answer. The use of other quantitative methods, such as cost-effectiveness analysis, may be helpful in modeling an optimal prophylaxis strategy.
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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 | 0.018 | 0.036 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.026 | 0.068 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| 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".