Prognostic Markers for Myeloid Neoplasms
Bibliographic record
Abstract
Myeloid neoplasms include cancers associated with both rapid (acute myeloid leukemias) and gradual (myelodysplastic syndromes and myeloproliferative neoplasms) disease progression. Percentage of blast cells in marrow is used to separate acute (rapid) from chronic (gradual) and is the most consistently applied prognostic marker in veterinary medicine. However, since there is marked variation in tumor progression within groups, there is a need for more complex schemes to stratify animals into specific risk groups. In people with acute myeloid leukemia (AML), pretreatment karyotyping and molecular genetic analysis have greater utility as prognostic markers than morphologic and immunologic phenotypes. Karyotyping is not available as a prognostic marker for AML in dogs and cats, but progress in molecular genetics has created optimism about the eventual ability of veterinarians to discern conditions potentially responsive to medical intervention. In people with myelodysplastic syndromes (MDS), detailed prognostic scoring systems have been devised that use various combinations of blast cell percentage, hematocrit, platelet counts, unilineal versus multilineal cytopenias and dysplasia, karyotype, gender, age, immunophenotype, transfusion dependence, and colony-forming assays. Predictors of outcome for animals with MDS have been limited to blast cell percentage, anemia versus multilineal cytopenias, and morphologic phenotype. Prognostic markers for myeloproliferative neoplasms (eg, polycythemia vera, essential thrombocythemia) include clinical and hematological factors and in people also include cytogenetics and molecular genetics. Validation of prognostic markers for myeloid neoplasms in animals has been thwarted by the lack of a large case series that requires cooperation across institutions and veterinary specialties. Future progress requires overcoming these barriers.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".