Factors Associated with the Occurrence of Epistaxis in Natural Canine Leishmaniasis ( <i>Leishmania infantum</i> )
Bibliographic record
Abstract
BACKGROUND: Canine leishmaniasis (CanL) is a common cause of epistaxis in dogs residing in endemic areas. The pathogenesis of CanL-associated epistaxis has not been fully explored because of the limited number of cases reported so far. HYPOTHESIS: Epistaxis in CanL could be attributed to more than 1 pathomechanism such as hemostatic dysfunction, biochemical abnormalities, chronic rhinitis, and coinfections occurring in various combinations. ANIMALS: Fifty-one dogs with natural CanL. METHODS: The allocation of 51 dogs in this cross-sectional study was based on the presence (n = 24) or absence (n = 27) of epistaxis. The potential associations among epistaxis and concurrent infections (Ehrlichia canis, Bartonella spp., and Aspergillus spp.), biochemical and hemostatic abnormalities, and nasal histopathology were investigated. RESULTS: Hypergammaglobulinemia (P= .044), increased serum viscosity (P= .038), decreased platelet aggregation response to collagen (P= .042), and nasal mucosa ulceration (P= .039) were more common in the dogs with epistaxis than in those without epistaxis. The other significant differences between the 2 groups involved total serum protein (P= .029) and gamma-globulin (P= .013) concentrations, which were higher, and the percentage platelet aggregation to collagen, which was lower (P= .012) in the epistaxis dogs. CLINICAL IMPORTANCE: CanL-associated epistaxis appears to be the result of multiple and variable pathogenetic factors such as thrombocytopathy, hyperglobulinemia-induced serum hyperviscosity, and nasal mucosa ulceration.
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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 | 0.000 | 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) | 0.001 | 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".