A case of canine renal lymphoma of granular lymphocytes with severe polycythemia
Bibliographic record
Abstract
BACKGROUND: Renal lymphoma in dogs is rare and has a poor prognosis. Granular lymphocyte morphology is rarely reported in canine renal lymphoma. Mild to moderate polycythemia is reported in a number of canine renal lymphoma cases. CASE PRESENTATION: A 10-year-old Labrador retriever presented to a university veterinary teaching hospital after a 1-month history of polyuria, polydipsia, and pollakiuria and a 2-week history of abdominal distention, lethargy, and increased respiratory effort. Abdominal ultrasound showed a wedge-shaped to rounded, heterogeneously hypoechoic mass lesion in the left kidney. Cytologic analysis of a percutaneous aspirate of the mass was consistent with lymphoma of granular lymphocytes. Severe polycythemia (hematocrit 0.871) was noted on a complete blood cell count. Clonality analysis identified a clonally rearranged T-cell receptor (TCR) gene and immunohistochemical staining was CD3+, CD79a- and CD11d+, supporting cytotoxic T-cell lymphoma. CONCLUSIONS: To our knowledge, this is the first report of renal cytotoxic T-cell lymphoma with severe polycythemia in a dog. Severe polycythemia and renal cytotoxic T-cell lymphoma are both rare in dogs; this report adds to the body of knowledge on these conditions.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".