Using Syndromic Surveillance to Estimate Baseline Rates for Healthcare-Associated Infections in Critical Care Units of Small Animal Referral Hospitals
Bibliographic record
Abstract
BACKGROUND: Expected rates of healthcare-associated infections (HCAI) have not been established in veterinary hospitals. Baseline rates are critically needed as benchmarks for quality animal care. OBJECTIVE: To estimate the occurrence of events related to HCAI identified using a standardized syndromic surveillance system in small animals in critical care cases at referral hospitals. ANIMALS: Weaned dogs and cats (n = 1,951) that were hospitalized in the critical care unit of referral teaching hospitals during a 12-week period. METHODS: Multicenter, prospective longitudinal study. A survey was completed for all enrolled animals to record basic demographics, information about procedures and treatments that animals received, and to document the occurrence of defined nosocomial syndromes. Data were analyzed to identify risk factors associated with the occurrence of these nosocomial syndromes. RESULTS: Controlling for hospital of admission, 16.3% of dogs (95% confidence intervals [CI], 14.3-18.5) and 12% of cats (95% CI, 9.3-15.5) were reported to have had ≥ 1 nosocomial syndrome occur during hospitalization. Risk factors found to have a positive association with the development of a nosocomial syndrome were longer hospital stays, placement of a urinary catheter, surgical procedures being performed, and the administration of antiulcer medications and antimicrobial drugs excluding those given perioperatively. CONCLUSIONS AND CLINICAL IMPORTANCE: Syndromic surveillance systems can be successfully standardized for use across multiple hospitals to effectively collect data pertinent to HCAI rates and risk factors for occurrence.
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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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".