Antimicrobial Prescribing in Dogs and Cats in Australia: Results of the Australasian Infectious Disease Advisory Panel Survey
Bibliographic record
Abstract
BACKGROUND: Investigations of antimicrobial use in companion animals are limited. With the growing recognition of the need for improved antimicrobial stewardship, there is urgent need for more detailed understanding of the patterns of antimicrobial use in this sector. OBJECTIVES: To investigate antimicrobial use for medical and surgical conditions in dogs and cats by Australian veterinarians. METHODS: A cross-sectional study was performed over 4 months in 2011. Respondents were asked about their choices of antimicrobials for empirical therapy of diseases in dogs and cats, duration of therapy, and selection based on culture and susceptibility testing, for common conditions framed as case scenarios: 11 medical, 2 surgical, and 8 dermatological. RESULTS: A total of 892 of the 1,029 members of the Australian veterinary profession that completed the survey satisfied the selection criteria. Empirical antimicrobial therapy was more common for acute conditions (76%) than chronic conditions (24%). Overall, the most common antimicrobial classes were potentiated aminopenicillins (36%), fluoroquinolones (15%), first- and second-generation cephalosporins (14%), and tetracyclines (11%). Third-generation cephalosporins were more frequently used in cats (16%) compared to dogs (2%). Agreement with Australasian Infectious Disease Advisory Panel (AIDAP) guidelines (generated subsequently) was variable ranging from 0 to 69% between conditions. CONCLUSIONS AND CLINICAL IMPORTANCE: Choice of antimicrobials by Australian veterinary practitioners was generally appropriate, with relatively low use of drugs of high importance, except for the empirical use of fluoroquinolones in dogs, particularly for otitis externa and 3rd-generation cephalosporins in cats. Future surveys will determine whether introduction of the 2013 AIDAP therapeutic guidelines has influenced prescribing habits.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".