Syndromic surveillance for evaluating the occurrence of healthcare‐associated infections in equine hospitals
Why this work is in the frame
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Bibliographic record
Abstract
REASONS FOR PERFORMING STUDY: Methods that can be used to estimate rates of healthcare-associated infections and other nosocomial events have not been well established for use in equine hospitals. Traditional laboratory-based surveillance is expensive and cannot be applied in all of these settings. OBJECTIVES: To evaluate the use of a syndromic surveillance system for estimating rates of occurrence of healthcare-associated infections among hospitalised equine cases. STUDY DESIGN: Multicentre, prospective longitudinal study. METHODS: This study included weaned equids (n = 297) that were admitted for gastrointestinal disorders at one of 5 participating veterinary referral hospitals during a 12-week period in 2006. A survey form was completed by the primary clinician to summarise basic case information, procedures and treatments the horse received, and whether one or more of 7 predefined nosocomial syndromes were recognised at any point during hospitalisation. Adjusted rates of nosocomial events were estimated using Poisson regression. Risk factors associated with the risk of developing a nosocomial event were analysed using multivariable logistic regression. RESULTS: Among the study population, 95 nosocomial events were reported to have occurred in 65 horses. Controlling for differences among hospitals, 19.7% (95% confidence interval, 14.5-26.7) of the study population was reported to have had at least one nosocomial event recognised during hospitalisation. The most commonly reported nosocomial syndromes that were unrelated to the reason for hospitalisation were surgical site inflammation and i.v. catheter site inflammation. CONCLUSIONS: Syndromic surveillance systems can be standardised successfully for use across multiple hospitals without interfering with established organisational structures, in order to provide useful estimates of rates related to healthcare-associated infections.
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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 | 0.005 | 0.005 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it