Association between oral health status and retrovirus test results in cats
Bibliographic record
Abstract
OBJECTIVE: To determine associations between oral health status and seropositivity for FIV or FeLV in cats. DESIGN: Cross-sectional survey. ANIMALS: 5,179 cats. PROCEDURES: Veterinarians at veterinary clinics and animal shelters completed online training on oral conditions in cats and then scored oral health status of cats with no known history of vaccination against FIV. Age, sex, and results of an ELISA for retroviruses were recorded. Results were analyzed by means of standard logistic regression with binary outcome. RESULTS: Of 5,179 cats, 237 (4.6%) and 186 (3.6%) were seropositive for FIV and FeLV, respectively, and of these, 12 (0.2%) were seropositive for FIV and FeLV. Of all 5,179 cats, 1,073 (20.7%) had gingivitis, 576 (11.1%) had periodontitis, 203 (3.9%) had stomatitis, and 252 (4.9%) had other oral conditions (overall oral disease prevalence, 2,104/5,179 [40.6%]). Across all age categories, inflammatory oral disease was associated with a significantly higher risk of a positive test result for FIV, compared with the seropositivity risk associated with other oral diseases or no oral disease. Stomatitis was most highly associated with risk of FIV seropositivity. Cats with any oral inflammatory disease were more likely than orally healthy cats to have a positive test result for FeLV. Increasing age was associated with a higher prevalence of oral disease in retrovirus-seronegative cats. CONCLUSIONS AND CLINICAL RELEVANCE: Inflammatory oral disease was associated with an increased risk of seropositivity for retroviruses in naturally infected cats. Therefore, retroviral status of cats with oral inflammatory disease should be determined and appropriate management initiated.
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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 | 0.000 |
| 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) | 0.002 | 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".