A multicenter study of antimicrobial prescriptions for cats diagnosed with bacterial urinary tract disease
Bibliographic record
Abstract
OBJECTIVES: The aim of this study was to evaluate initial antimicrobial therapy in cats diagnosed with upper or lower bacterial urinary tract infections at veterinary practices in the USA and Canada. METHODS: Electronic medical records from a veterinary practice corporation with clinics in the USA and Canada were queried between 2 January 2016 and 3 December 2018. Feline patient visits with a diagnosis field entry of urinary tract infection, cystitis and pyelonephritis, as well as variation of those names and more colloquial diagnoses such as kidney and bladder infection, and where an antimicrobial was prescribed, were retrieved. RESULTS: Prescription data for 5724 visits were identified. Sporadic cystitis was the most common diagnosis (n = 5051 [88%]), with 491 (8.6%) cats diagnosed with pyelonephritis and 182 (3.2%) with chronic or recurrent cystitis. Cefovecin was the most commonly prescribed antimicrobial for all conditions, followed by amoxicillin-clavulanic acid. Significant differences in antimicrobial drug class prescribing were noted between practice types and countries, and over the 3-year study period. For sporadic cystitis, prescription of amoxicillin-clavulanic acid increased significantly and cefovecin decreased between 2016 and 2018, and 2017 and 2018, while fluoroquinolone use increased between 2017 and 2018. CONCLUSIONS AND RELEVANCE: The results indicate targets for intervention and some encouraging trends. Understanding how antimicrobials are used is a key component of antimicrobial stewardship and is required to establish benchmarks, identify areas for improvement, aid in the development of interventions and evaluate the impact of interventions or other changes.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".