Prevalence of antibiotic use for dogs and cats in United States veterinary teaching hospitals, August 2020
Bibliographic record
Abstract
BACKGROUND: Awareness of prescribing practices helps identify opportunities to improve antibiotic use (AU). OBJECTIVES: To estimate AU prevalence in dogs and cats in U.S. veterinary teaching hospitals (VTHs) and identify antibiotic drugs commonly prescribed, indications for use, and evidence of bacterial infection. ANIMALS: Medical record data were collected from dogs and cats examined at 14 VTHs. METHODS: Data were collected from VTH medical records of dogs and cats examined by primary care, urgent care, emergency and critical care, internal medicine, and surgery services on a single day during August 13-September 3, 2020. Data included signalment; clinical service; inpatient or outpatient status; clinical conditions; diagnostic tests; evidence of bacterial infection; intended reason for AU; name and route of antibiotics prescribed. RESULTS: Of 883 dogs and cats, 322 (36.5%) were prescribed at least 1 antibiotic. Among 285 antibiotics administered systemically intended for treatment of infection, 10.9% were prescribed without evidence of infection. The most common class of antibiotics presribed for systemic administration was potentiated penicillin for dogs (115/346, 33.3%) and cats (27/80, 33.8%). For dogs and cats, first-generation cephalosporins (93/346, 26.9% and 11/80, 13.8%, respectively) and fluoroquinolones (51/346, 14.7% and 19/80, 23.8%, respectively) was second or third most-prescribed. Common AU indications included skin, respiratory, and urinary conditions, and perioperative use. CONCLUSIONS AND CLINICAL IMPORTANCE: Collaborative data collection provides a sustainable methodology to generate national AU prevalence estimates and bring attention to areas requiring additional research and detailed data collection. These efforts can also identify practice improvement opportunities in settings where future veterinarians are trained.
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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 | 0.000 |
| 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 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".