Chlorhexidine Gluconate Bathing Has Limited Ability to Prevent Surgical Site Infection Following Operative Fixation of Extremity and Pelvic Fractures
Bibliographic record
Abstract
BACKGROUND: A preoperative chlorhexidine gluconate (CHG) bath is used to reduce the risk of surgical site infection (SSI) in elective surgery, but its efficacy in the trauma setting is unknown. We compared the incidence of SSI between patients who did versus did not receive a CHG bath before operative fixation of extremity and/or pelvic fractures. METHODS: We conducted a secondary analysis of the PREP-IT cluster-randomized crossover trials that enrolled patients undergoing operative treatment for open or closed extremity or pelvic fractures. Preoperative CHG bathing (yes or no) was recorded for each patient per study protocol. The association between CHG bathing and SSI within 90 days after definitive fracture surgery was assessed. We performed multivariable regression to adjust for prognostic variables. We also conducted a separate instrumental variable analysis to compare SSI rates between study sites that used CHG bathing for >90% of participants and those that used CHG bathing for <1% of participants. RESULTS: Of the 10,103 participants (mean age, 51 ± 20 years; 47% female; 77% White; 17% Black; 4% Asian; 7% Hispanic) included in the analysis, 2,674 (26%) had a documented preoperative CHG bath and 7,429 (74%) did not. CHG bathing was not associated with a significant reduction in the odds of 90-day SSI in the multivariable (odds ratio [OR], 1.07; 95% confidence interval [CI], 0.86 to 1.32; p = 0.56) or instrumental variable (OR, 0.88; 95% CI, 0.62 to 1.25; p = 0.48) analyses. CONCLUSIONS: Among adult patients who underwent extremity or pelvic fracture surgery, preoperative CHG bathing was not associated with a significant reduction in SSI. 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 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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".