Anti-Endothelial Cell Antibodies in Dogs with Immune-Mediated Hemolytic Anemia and Other Diseases Associated with High Risk of Thromboembolism
Bibliographic record
Abstract
BACKGROUND: Dogs with immune-mediated hemolytic anemia (IMHA) and certain inflammatory diseases are at high risk of developing thromboembolic disease. The presence of anti-endothelial cell autoantibodies (AECA) has been associated with an increased risk of thromboembolism in humans. HYPOTHESIS: AECA will be detected more often in dogs at risk of thromboembolism than in healthy control animals or dogs with diseases not associated with a higher risk of thromboembolism. ANIMALS: Ninety-one sick dogs and 22 healthy control dogs. METHODS: Retrospective case-controlled study. Serum was screened for the presence of AECA. Dogs were identified for the study based on the risk of thromboembolism as determined by clinical impression and the underlying disease process. Flow cytometry and normal canine endothelial cells were used to screen serum samples from sick and healthy control dogs for the presence of AECA. In addition, serum from dogs with confirmed thromboemboli was also screened for the presence of AECA by immunohistochemistry. RESULTS: AECA were detected in 2/91 sick dogs, both with infectious diseases, but were not found in healthy dogs. Anti-endothelial antibodies were not detected in 21 dogs with IMHA and 20 dogs with systemic inflammatory response syndrome, sepsis, or both. CONCLUSIONS: We conclude that AECA are rarely detectable in dogs considered at high risk of thromboembolism. These findings suggest that AECA may not play an important role in the pathogenesis of thromboembolism in dogs with IMHA and other inflammatory diseases.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".