Prevalence of antinuclear and anti‐erythrocyte antibodies in healthy cats
Bibliographic record
Abstract
BACKGROUND: Positive antinuclear antibody and direct antiglobulin tests support diagnoses such as systemic lupus erythematosus and immune-mediated anemia, respectively. Positive tests may occur in cats, but the prevalence of positive results in healthy cats is not well known. OBJECTIVE: The study's purpose was to determine prevalences of positive antinuclear antibody and direct antiglobulin tests in healthy cats. METHODS: Antinuclear antibody titers were measured by indirect immunofluorescence, and anti-erythrocyte antibodies were measured by the microtitration direct antiglobulin test at 37, 23, and 4°C in 61 client-owned and 28 facility-owned cats. Differences between the 2 groups were examined using chi-squared tests. RESULTS: For the antinuclear antibody tests, 70% of client-owned cats were negative, 10% had weak titers (1:40-1:80), and 20% had strong titers (1:160-1:320). Facility-owned cats had significantly fewer positive titers with 96% negative and one positive (1:8). For the antiglobulin test at 37°C, 93% of all cats were negative, 2 cats in each group were positive at low dilutions (1:2), and 2 client-owned cats were transiently positive at high dilutions (≥ 1:2048). At 23°C, 90% of all cats were negative, and 2 client-owned and 5 facility-owned cats were positive at low dilutions (1:2-1:8). At 4°C, 67% of client-owned cats had invalid results (negative control well agglutination), and 33% had negative results, while of facility-owned cats 14% had invalid results, 14% had agglutination at low dilutions, and 72% were negative. CONCLUSION: Healthy cats may have positive antinuclear antibody and direct antiglobulin tests, but the prevalence of strong reactions is low.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".