Perinuclear antineutrophil cytoplasmic autoantibodies in dogs infected with various vector-borne pathogens and in dogs with immune-mediated hemolytic anemia
Bibliographic record
Abstract
OBJECTIVE: To determine the prevalence of perinuclear antineutrophil cytoplasmic autoantibodies (pANCA) in dogs with confirmed or suspected immune-mediated hemolytic anemia (IMHA) or dogs infected with various vector-borne pathogens, including Rickettsia rickettsii, Bartonella henselae, Bartonella vinsonii subsp berkhoffii, Ehrlichia canis, Borrelia burgdorferi, and Leishmania infantum. ANIMALS: 55 dogs with confirmed or suspected IMHA, 140 dogs seroreactive for vector-borne pathogens, and 62 healthy dogs and dogs seronegative for vector-borne pathogens. PROCEDURES: Samples were allocated to subgroups on the basis of the health status of the dogs and the degree of seroreactivity against various vector-borne pathogens. Serum samples were tested retrospectively via indirect immunofluorescence assay to determine pANCA status. RESULTS: 26 of 55 (47%) dogs with confirmed or suspected IMHA and 67 of 140 (48%) dogs seroreactive for vector-borne pathogens had positive results when tested for pANCA. Serum samples with the highest antibody concentrations against L infantum antigen had the highest proportion (28/43 [65%]) that were positive for pANCA. One of 20 (5%) dogs seronegative for tick-borne pathogens and 8 of 22 (36%) dogs seronegative for L infantum had positive results for pANCA. One of 20 (5%) healthy dogs had serum antibodies against pANCA. CONCLUSIONS AND CLINICAL RELEVANCE: pANCA were detected in a high percentage of dogs with IMHA and vector-borne infectious diseases. Therefore, pANCA may be a relatively nonspecific marker for dogs with inflammatory bowel disease, although they could represent a biomarker for immune-mediated diseases and infections.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".