Comparison of chlorhexidine and alcohol‐based antisepsis of the distal limbs of horses
Bibliographic record
Abstract
Abstract Background An alcohol‐based rub has been confirmed effective at reducing bacterial counts on equine skin. Skin sites with expected high bacterial burden have not been tested or has a comparison to a common protocol been performed. Objectives To determine if ethanol‐based antisepsis reduces bacterial counts on the equine distal limb comparable to a current chlorhexidine scrub method and determine the most effective application technique for the product. Study design Randomised trial. Methods Forty‐one horses were used in the study. By horse, each limb was randomly assigned to a treatment group: 5min scrub using 4% chlorhexidine gluconate to a clipped site (CHG); 90s scrub using 80% ethanol to a clipped site (ETC); 90s contact with 80% ethanol applied as a spray to a clipped site (ETS) and 90s scrub using 80% ethanol to an unclipped site (ETUC). Samples were collected pre‐ and post‐treatment and plated in duplicate. Bacterial counts were log 10 transformed and averaged between duplicates. A linear mixed model was used to compare mean log 10 CFU/mL reduction between groups. A cost‐benefit analysis was performed. Results There was no significant difference in mean log 10 CFU/mL reduction between CHG and ETC in either fore‐ or hindlimbs. In forelimbs, there was no significant difference in mean log 10 CFU/mL reduction between any groups. In hindlimbs, CHG had significantly greater mean log 10 CFU/mL reduction than ETUC and ETS. No significant difference in cost‐benefit was found between CHG and ETC. Significant differences were noted between CHG and both ETUC and ETS. Main limitations Researchers were not blinded to treatment group during sample collection. Conclusions This study showed no significant difference in reduction in bacterial counts on the distal limb of horses between CHG and ethonol (ET) when applied as a scrub to a clipped site and there was no significant difference in cost‐benefit between these treatments.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".