A Practical, Comprehensive Classification for Pediatric Myelodysplastic Syndromes: The CCC System
Bibliographic record
Abstract
PURPOSE: Pediatric myelodysplastic syndromes (MDS) are biologically diverse. The French-American-British (FAB) classification of adult forms of MDS is not always applicable because many pediatric patients do not fit into any of the categories. To circumvent the FAB schema and other flawed formats, the authors developed a practical classification system for childhood MDS. PATIENTS AND METHODS: The authors analyzed 40 children with MDS diagnosed in Toronto between 1988 and 1998 to test the utility of the classification. Children were classified according to three main features: category, cytology, and cytogenetics. RESULTS: Using this system the authors were able to classify all 40 patients; about half could not be classified by FAB. Patients could also be longitudinally classified by serial analysis to show progression of disease. Juvenile myelomonocytic leukemia was excluded because of its known myeloproliferative pathogenesis. Chronic myelomonocytic leukemia, which almost never occurs in children, was also omitted. Also excluded were other chronic myeloproliferative disorders and any cytopenias without malignant potential. CONCLUSIONS: Based on these data, the CCC system appears to have prognostic potential; children with advanced class and cytogenetic abnormalities had a poorer outcome. The authors urge international adoption of this system for uniformity in clinical practice and reporting purposes.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 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 | 0.001 |
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".