Acute Leukemia in Horses
Bibliographic record
Abstract
Leukemia is broadly divided into acute and chronic lymphocytic and myeloid types based on the proportion of blasts, morphology of cells, and expression of specific antigens on neoplastic cells. Classifying leukemia in horses can be challenging if blasts predominate and since few antibodies to identify cell types are available. The objective of this study was to describe in detail the clinical and pathologic features of acute leukemia in horses. Twelve horses ranging from 0.2 to 25.9 years of age were diagnosed with acute leukemia. Six cases were classified as acute lymphocytic leukemia (ALL) based on predominance of blasts, lack of granulocytic or monocytic differentiation, and detection of CD3, CD20, and/or CD79a antigens by immunohistochemistry. Six other cases were classified as acute myeloid leukemia (AML) with myelomonocytic ( n = 4), basophilic ( n = 1), and eosinophilic ( n = 1) differentiation based on > 20% bone marrow blasts and partial leukocytic differentiation. Reactivity with antibodies to Iba-1/AIF-1, CD172a, and CD163 was determined for all cases of AML. Eleven horses had thrombocytopenia, 10 had neutropenia, 8 had anemia, all had blasts on blood films, and none had leukocytosis. Ten horses had increased serum acute phase proteins. Bone marrow cellularity ranged from 30% to 100%, and the proportion of blasts ranged from 80% to 100% and 30% to 60% in ALL and AML, respectively. Horses were severely ill at diagnosis and euthanized within days or weeks. Unique features of acute leukemia in horses compared to other species were variable lymphocyte antigen expression (ALL) and frequent inflammation (ALL and AML).
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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) | 0.000 | 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) | 0.000 | 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".