2018 AAHA Infection Control, Prevention, and Biosecurity Guidelines*
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it