Seroprevalences of feline leukemia virus and feline immunodeficiency virus infection in cats in the United States and Canada and risk factors for seropositivity
Bibliographic record
Abstract
OBJECTIVE To estimate seroprevalences for FeLV antigen and anti-FIV antibody and risk factors for seropositivity among cats in the United States and Canada. DESIGN Cross-sectional study. ANIMALS 62,301 cats tested at 1,396 veterinary clinics (n = 45,406) and 127 animal shelters (16,895). PROCEDURES Blood samples were tested with a point-of-care ELISA for FeLV antigen and anti-FIV antibody. Seroprevalence was estimated, and risk factors for seropositivity were evaluated with bivariate and multivariable mixed-model logistic regression analyses adjusted for within-clinic or within-shelter dependencies. RESULTS Overall, seroprevalence was 3.1% for FeLV antigen and 3.6% for anti-FIV antibody. Adult age, outdoor access, clinical disease, and being a sexually intact male were risk factors for seropositivity for each virus. Odds of seropositivity for each virus were greater for cats tested in clinics than for those tested in shelters. Of 1,611 cats with oral disease, 76 (4.7%) and 157 (9.7%) were seropositive for FeLV and FIV, respectively. Of 4,835 cats with respiratory disease, 385 (8.0%) were seropositive for FeLV and 308 (6.4%) were seropositive for FIV. Of 1,983 cats with abscesses or bite wounds, 110 (5.5%) and 247 (12.5%) were seropositive for FeLV and FIV, respectively. Overall, 2,368 of 17,041 (13.9%) unhealthy cats were seropositive for either or both viruses, compared with 1,621 of 45,260 (3.6%) healthy cats. CONCLUSIONS AND CLINICAL RELEVANCE Seroprevalences for FeLV antigen and anti-FIV antibody were similar to those reported in previous studies over the past decade. Taken together, these results indicated a need to improve compliance with existing guidelines for management of feline retroviruses.
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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 | 0.001 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".