Evaluation of antimicrobial purchasing by companion animal veterinary facilities in Canada, the United Kingdom, and the United States of America (2019-2021)
Bibliographic record
Abstract
BACKGROUND: Measuring antimicrobial use is a core component of antimicrobial stewardship. Purchasing data may be easier to obtain than prescription data in some situations, but differences in clinic size, caseload and collection timeframes must be considered. OBJECTIVE: Our objective was to evaluate purchases of systemic antibacterial agents by small animal veterinary facilities in 5 networks across 3 countries, using a mg/veterinarian full time equivalent (FTE)/week as the metric. METHODS: Data were obtained from purchasing records of 2194 veterinary facilities from networks from the United States (US, n = 3: US-A, 1036 facilities; US-B, 101 facilities; US-C, 886 facilities), Canada (n = 1: 117 facilities) and the United Kingdom (UK, n = 1: 54 facilities) during 2019-2021. RESULTS: In total, 20 020 269 767 mg (20.02 t) of antimicrobials were purchased. Overall differences between the UK and North America were driven by significantly higher purchases of amoxicillin-clavulanic acid in the UK (P < .001), with substantially less purchasing of third generation cephalosporins in the UK (P < .0001). A significant association was found between FTE and purchasing, with decreased purchasing (mg/FTE/week) as facility FTE increased. Significant differences also were found among US regions. Facilities in the top 10% of total purchasing accounted for 23%-30% of purchases, compared to only 1.6%-3.8% for the bottom 10%. CONCLUSIONS AND CLINICAL IMPORTANCE: These data provide useful information about general purchasing trends, inter- and intraregional differences and differences among facility types and identify high purchasing outliers for further investigation.
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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 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".