2025 FelineVMA feline oral health and dental care guidelines
Bibliographic record
Abstract
Oral and dental diseases are commonplace in cats, imposing a responsibility on primary care veterinarians to provide high quality oral healthcare for their feline patients. While patient assessment begins with an examination of the conscious cat, further assessment under anesthesia is necessary for the purposes of radiography and treatment, making anesthesia an essential component of feline dentistry. Because feline patients with oral and dental diseases, as well as those convalescing from surgery, generally experience pain, multimodal perioperative analgesia and anesthesia are standard features of oral and dental care. The '2025 FelineVMA feline oral health and dental care guidelines' are coauthored by a Task Force of board-certified veterinary specialists and a veterinary technician specialist in dentistry convened by the Feline Veterinary Medical Association (FelineVMA). These experts have compiled evidence-guided recommendations for optimal oral health and dental care, including therapeutic interventions, in general feline practice. The focus is on the most commonly encountered oral and dental diseases in cats. These include periodontal disease, early-onset gingivitis, tooth resorption, endodontic disease and tooth trauma, feline chronic gingivostomatitis, developmental abnormalities such as malocclusion, and oral masses and growths, as well as various miscellaneous conditions. An extensive bibliography provides additional resources that extend beyond the topics reviewed in these Guidelines. Caregivers should be active participants in their cat's oral and dental healthcare. Veterinary team members can empower their patients' caregivers by educating them on signs of oral and dental disease in their cats and by providing home care guidance for maintaining oral and dental health. In any high-performing practice that cares for cats, the entire practice team are advocates for oral and dental care, and are knowledgeable about the principles of prevention and treatment of this important assortment of diseases.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.006 | 0.002 |
| Insufficient payload (model declined to judge) | 0.047 | 0.021 |
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".