2018 AAHA Infection Control, Prevention, and Biosecurity Guidelines*
Bibliographic record
Abstract
A veterinary team's best work can be undone by a breach in infection control, prevention, and biosecurity (ICPB). Such a breach, in the practice or home-care setting, can lead to medical, social, and financial impacts on patients, clients, and staff, as well as damage the reputation of the hospital. To mitigate these negative outcomes, the AAHA ICPB Guidelines Task Force believes that hospital teams should improve upon their current efforts by limiting pathogen exposure from entering or being transmitted throughout the hospital population and using surveillance methods to detect any new entry of a pathogen into the practice. To support these recommendations, these practice-oriented guidelines include step-by-step instructions to upgrade ICPB efforts in any hospital, including recommendations on the following: establishing an infection control practitioner to coordinate and implement the ICPB program; developing evidence-based standard operating procedures related to tasks performed frequently by the veterinary team (hand hygiene, cleaning and disinfection, phone triage, etc.); assessing the facility's ICPB strengths and areas of improvement; creating a staff education and training plan; cataloging client education material specific for use in the practice; implementing a surveillance program; and maintaining a compliance evaluation program. Practices with few or no ICPB protocols should be encouraged to take small steps. Creating visible evidence that these protocols are consistently implemented within the hospital will invariably strengthen the loyalties of clients to the hospital as well as deepen the pride the staff have in their roles, both of which are the basis of successful veterinary practice.
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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 | 0.010 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.012 | 0.010 |
| Insufficient payload (model declined to judge) | 0.052 | 0.039 |
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".