Immunophenotypic Classification of Leukemia in 3 Horses
Bibliographic record
Abstract
eukemia, a neoplastic disease of the hematopoietic sys- tem, is rare in horses, and reported forms include lymphocytic, myelomonocytic, monocytic, granulocytic, and eosinophilic leukemia. 13][4][5][6][7][8] Monoclonal antibodies that recognize specific equine leukocyte cell surface antigens can be used to immunophenotype the neoplastic cells and more accurately characterize their lineage.0][11] Because prognosis and management of leukemia depend in part on the lineage and maturity of the leukemic cells, improved characterization with immunophenotyping is needed to classify equine leukemias.This report describes the clinical and diagnostic findings, including immunophenotyping, of leukemia in 3 horses.A 20-year-old 505-kg Arabian/Quarterhorse gelding was presented to the University of Wisconsin-Madison Veterinary Medical Teaching Hospital (VMTH) on September 21, 1995, with a complaint of chronic weight loss for 2 years, recurrent skin infections, long hair coat, and lethargy.Physical examination identified a grade 3/6 holosystolic left heart base murmur, a dull dry hair coat with patches of alopecia, and muscle wasting evident over the spine, scapula, and hips.Lymphadenopathy of submandibular, retropharyngeal, and right caudal cervical lymph nodes was present.Initial diagnostic testing included a CBC (Table 1), serum chemistry profile, and urinalysis.Abnormal findings
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".