Tongue nodules in canine leishmaniosis — a case report
Bibliographic record
Abstract
BACKGROUND: Canine leishmaniosis (CanL) caused by Leishmania infantum is an endemic zoonosis in southern European countries. Infected dogs can present rare or atypical forms of the disease and diagnosis can be challenging. The present report describes a case of tongue nodules in a 3-year-old neutered female Labrador Retriever dog with leishmaniosis. FINDINGS: A fine needle aspiration of the lingual nodules revealed amastigote forms of Leishmania inside macrophages. Differential diagnosis ruled out neoplasia, calcinosis circumscripta, solar glossitis, vasculitis, amyloidosis, eosinophilic granulomas, chemical and electrical burns, uremic glossitis and autoimmune diseases. Combined therapy with antimoniate meglumine and allopurinol for 30 days resulted in the normalization of hematological and biochemical parameters. Two months after diagnosis and the beginning of treatment, a mild inflammatory infiltrate was observed by histopathology, but an anti-Leishmania immunofluorescence antibody test (IFAT) was negative as well as a PCR on both tongue lesions and a bone marrow aspirate. Seven months after diagnosis, the dog's general condition appeared good, there were no tongue lesions and a new IFAT was negative. Fifteen months after diagnosis this clinically favourable outcome continued. CONCLUSIONS: The dog could have suffered a relapsing episode of CanL, but a new systemic or local infection cannot be excluded. Regular clinical re-evaluation should be maintained, as a future relapse can potentially occur. In conclusion, CanL should be considered in the differential diagnosis of nodular glossitis in dogs.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".