Diagnosis and treatment of pediatric myelodysplastic syndromes: A survey of the North American Pediatric Aplastic Anemia Consortium
Bibliographic record
Abstract
BACKGROUND: Myelodysplastic syndromes (MDS) represent a group of clonal hematopoietic stem cell disorders that commonly progress to acute myeloid leukemia (AML). The diagnostics, prognostics, and treatment of adult MDS are established but do not directly translate to children and adolescents. Pediatric MDS is a rare disease, characterized by unique cytogenetics and histology compared with adult MDS, and often arises secondary to germline predisposition or cytotoxic exposures. Our objective was to highlight aspects of diagnosis/management that would benefit from further systematic review toward the development of clinical practice guidelines for pediatric MDS. PROCEDURE: The North American Pediatric Aplastic Anemia Consortium (NAPAAC) is composed of collaborative institutions with a strong interest in pediatric bone marrow failure syndromes and hematologic malignancies. The NAPAAC MDS working group developed a national survey distributed to 35 NAPAAC institutions to assess data on (1) clinical presentation of pediatric MDS, (2) diagnostic evaluation, (3) criteria for diagnosis, (4) supportive care and treatment decisions, and (5) role of hematopoietic stem cell transplantation (HSCT). RESULTS: Twenty-eight of 35 institutions returned the survey. Most centers agreed on a common diagnostic workup, though there was considerable variation regarding the criteria for diagnosis. Although there was consensus on supportive care, treatment strategies, including the role of cytoreduction and HSCT, varied across centers surveyed. CONCLUSIONS: There is lack of national consensus on diagnosis and treatment of pediatric MDS. This survey identified key aspects of MDS management that will warrant systematic review toward the goal of developing national clinical practice guidelines for pediatric MDS.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".