Antimicrobial use practices in canine and feline dental procedures performed in primary care veterinary practices in the United States
Bibliographic record
Abstract
This study examined the utilization of antimicrobials in canines and felines receiving dental treatments in veterinary clinics in the United States, retrospectively. A total of 818,150 animals (713,901 procedures in dogs and 104,249 procedures in cats) underwent dental procedures under general anesthesia in 2020. These included dental prophylaxis and extractions. Patient demographic data, antimicrobial treatment, treatment duration, dose, periodontal disease score, whether tooth extractions were performed and how many extractions were performed was recorded. Our results showed that local or systemic antimicrobials were used in 116,723/713,901 (16.4%) procedures in dogs and 14,264/104,249 (14%) procedures in cats. Age, weight, extraction of one or more teeth and diagnosis of periodontal disease (any stage) were associated with increased likelihood of antimicrobial administration using univariable analysis (all P<0.001) and in the multivariable model. Clindamycin, amoxicillin-clavulanate and amoxicillin were the most common oral antimicrobials used in dogs and cats. Drugs classified as highest priority clinically important antibiotics (HPCIA) were administered to 30,960/116,723 (26.5%) of dogs and 7,469/14,264 (52%) of treated cats. The results obtained can inform interventions to optimize patient care and promote prudent use of antimicrobials during dental procedures in canine and feline patients.
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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 | 0.000 | 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".