The Use of Waterless Alcohol-based Antiseptic for Surgical Skin Preparation in Rhesus Macaques (<i>Macaca mulatta</i>)
Bibliographic record
Abstract
Ensuring asepsis of the surgical site before surgery is an essential component of safe surgical practices to reduce the incidence of surgical site infections in veterinary medicine. The current accepted method of skin preparation is a multistep process that alternates either a povidone-iodine or chlorhexidine soap scrub with a 70% alcohol rinse. After cleansing, the site is left to dry before draping. The goal of this study was to assess the effectiveness of a waterless alcohol-based (WAB) antiseptic as part of a 2-step procedure after the soap scrub. WAB antiseptics are commonly used as a presurgical hand scrub for the surgeon as they evaporate quickly and provide effective antisepsis. Previous studies have examined the WAB antiseptics in small animal surgeries. We tested this approach in large animal surgery. Twenty-four rhesus macaques were divided into 4 groups that received one of the following treatments: saline and alcohol, iodine-alcohol-iodine, soap scrub/WAB, and chlorhexidine-alcohol-chlorhexidine. The surgical site was swabbed before and after treatment and plated to assess sterility. Overall, no colonies were recovered from skin treated with WAB antiseptic, establishing it as an effective alternative to the current standard protocol. This method will simplify the current 3-step procedure and reduce animal handling, the use of materials, and the time necessary for surgical preparation.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".