Antimicrobial use practices in canine and feline patients with co-morbidities undergoing dental procedures in primary care practices in the US
Bibliographic record
Abstract
This study aimed to investigate how the presence of co-morbid conditions influenced antimicrobial usage as presumptive prophylaxis for suspected bacteremia in dogs and cats undergoing dental treatments at primary care veterinary clinics in the United States. In 2020, data was collected from 1076 veterinary clinics across 44 US states. A total of 681,541 general anesthesia dental procedures were conducted on 592,472 dogs and 89,069 cats. This revealed that systemic antimicrobials were administered in 8.8% of dog procedures and 7.8% of cat procedures in the absence of concurrent periodontal disease or extractions. Cefpodoxime, clindamycin, and amoxicillin-clavulanate were the most frequently used antimicrobials in dogs, while cefovecin, amoxicillin-clavulanate, and clindamycin topped the list for cats. Dogs with cardiovascular, hepato-renal, and endocrine co-morbidities, as well as those undergoing concurrent removal of cutaneous or subcutaneous neoplasia, displayed higher antimicrobial use. Similarly, cats with endocrine or hepato-renal disease, retroviral infection (i.e., feline leukemia virus (FeLV), feline immunodeficiency virus (FIV)), and concurrent removal of cutaneous or subcutaneous neoplasia exhibited increased antimicrobial use. Dogs with hepato-renal abnormalities had longer treatment durations compared to those without (10.1 vs. 9.6 days). Conversely, cats with concurrent removal of cutaneous or subcutaneous neoplasia had shorter durations of treatment as compared to those that did not have this procedure performed (8.4 vs 9.2 days). The findings of this study underscore the necessity for further research and collaboration within the veterinary community to develop evidence-based guidelines, promoting responsible antimicrobial use, and advancing the field of veterinary dentistry for enhanced patient outcomes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".