The impact of category, cytopathology and cytogenetics on development and progression of clonal and malignant myeloid transformation in inherited bone marrow failure syndromes
Bibliographic record
Abstract
Inherited bone marrow failure syndromes are a group of rare, heterogeneous genetic disorders with a risk of clonal and malignant myeloid transformation including clonal marrow cytogenetic abnormalities, myelodysplastic syndrome and acute myeloid leukemia. The clinical characteristics, risk classification, prognostic factors and outcome of clonal and malignant myeloid transformation associated with inherited bone marrow failure syndromes are largely unknown. The aims of this study were to determine the impact of category, cytopathology and cytogenetics, the three components of the "Category Cytology Cytogenetics" classification of pediatric myelodysplastic syndrome, on the outcome of clonal and malignant myeloid transformation associated with inherited bone marrow failure. We used data from the Canadian Inherited Marrow Failure Registry. Among 327 patients with inherited bone marrow failure syndrome enrolled in the registry, the estimated risk of clonal and malignant myeloid transformation by the age of 18 years was 37%. The risk of clonal and malignant myeloid transformation varied according to the type of inherited bone marrow failure syndrome but was highest in Fanconi anemia. The development of clonal and malignant myeloid transformation significantly affected overall survival. Mortality varied based on cytopathological group. The largest group of patients had refractory cytopenia. Clonal marrow cytogenetic abnormalities were identified in 87% of patients with clonal and malignant myeloid transformation, and different cytogenetic groups had different impacts on disease progression. We conclude that category, cytopathology and cytogenetics in cases of clonal and malignant myeloid transformation associated with inherited bone marrow failure syndromes have an important impact on outcome and that the classification of such cases should incorporate these factors.
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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 | 0.001 | 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".