Antimicrobial Prescribing Practices Among Sri Lankan Veterinarians for Common Medical Conditions in Companion Animals
Bibliographic record
Abstract
Veterinarians are essential in antimicrobial stewardship. Companion animal (CA) practitioners have recently received more attention. There are few relevant studies on CA antimicrobial prescribing practices in South Asia. A cross-sectional survey on the antimicrobial prescribing practices of Sri Lankan veterinarians associated with six common CA infections and factors associated with bacterial culture and antimicrobial susceptibility testing (AST) was conducted. A survey of antimicrobial prescribing practices was distributed via email or professional events among veterinarians treating CAs. There were 127 respondents. Most were from CA-only practices (63%); the remainder worked in government, mixed, or other practices. Based on the World Health Organization's criteria, 1.7% (13/783) of cases were treated with drugs authorized for human use only; 23.9% (187/783) were treated with highest-priority critically important antimicrobials; 4.7% (37/783) were treated with critically important antimicrobials, and 66.8% (523/783) were treated with highly important antimicrobials. The antimicrobial treatment of abscesses, skin, urinary, and ear infections was usually empirical, with only 12.4% (72/579) of cases utilizing AST to guide diagnosis and treatment. The use of AST was significantly associated with the type of antibiotic prescribed, the medical condition, the practice type, and continuing professional development, with the latter recognized as a pathway toward improved veterinary antimicrobial stewardship and the implementation of a One Health approach.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".