Chemotherapy in canine acute megakaryoblastic leukemia: a case report and review of the literature.
Bibliographic record
Abstract
Acute myeloid leukemia (AML) in dogs is a rare disease with poor prognosis. In most subjects, palliative treatment or euthanasia is performed. A 3.5-year-old male castrated labrador with AML-M7, which was treated with induction polychemotherapy (8 cycles) using vincristine (0.5 mg/m(2)/cycle), daunorubicin (20 mg/m(2)/cycle), cytosine arabinoside (ARA-C, 100 mg/m(2)/cycle) and prednisolone (1 mg/kg/day) is reported. Treatment was well tolerated and complete remission was achieved. Postinduction chemotherapy consisted of ARA-C, daunorubicin and prednisolone. After 3, 5 and 18 months, the subject relapsed. Each relapse was treated with ARA-C (up to 1,000 mg/m(2)) and etoposide or daunorubicin. Again, no severe side-effects occurred and the disease was controlled, with 37 chemotherapy-cycles (ARA-C, 3 x 1,000 mg/m(2)/cycle), for 24 months. Based on a literature-search, this is the first report documenting a long-term response of canine AML, probably resulting from the high-dose ARA-C. Clinical trials using high-dose ARA-C are now required to confirm antileukemic efficacy in canine leukemias.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".