Antimicrobial dispensing for common conditions in dogs and cats at a large veterinary practice network, 2023
Bibliographic record
Abstract
Understanding antimicrobial use is a core component of antimicrobial stewardship. This study aimed to assess antimicrobial dispensing for common clinical conditions in dogs and cats presented to veterinary clinics belonging to a large clinic network in the USA. Antimicrobials were prescribed for 831,017 patient visits, to 702,576 (85 %) dogs and 128,441 (15 %) cats. Cefpodoxime (n = 203,145, 29 %), amoxicillin-clavulanate (154,779, 22 %) and metronidazole (150,830, 21 %) were the most commonly dispensed antimicrobials in dogs, while cefovecin (55,579, 43 %) and amoxicillin-clavulanate (44,857, 35 %) predominated in cats. In dogs, drugs classified by the WHO MIA List as highest priority critically important (HPCIA) accounted for 39 % of drugs dispensed, while those classified as highly important (HIA) accounted for 61 %. In cats, HPCIA drugs accounted for 46 % of drugs dispensed while HIA drugs accounted for 54 %. Consistency of drug selection with selected treatment guidelines was 76 % (30,562/40,375) for dogs and 57 % (12,810/22,644) for cats. There were regional differences in drug selection patterns for all of the 10 most common diseases, for both dogs and cats. While no single metric or data source provides a full understanding of antimicrobial use, these data provide the foundation for assessment of antimicrobial use practices and provide insight and baseline data for development of interventions to improve antimicrobial use practices.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".