How I treat ALL in Down's syndrome: pathobiology and management
Bibliographic record
Abstract
Children with Down syndrome are at high risk for developing B-cell precursor acute lymphoblastic leukemia (DS-ALL) associated with poor outcome due to both a high relapse rate and increased treatment-related mortality (TRM) from infections. Biologically, these heterogeneous leukemias are characterized by under-representation of the common cytogenetic subgroups of childhood ALL and overrepresentation of CRLF2-IL7R-JAK-STAT activating genetic aberrations. Although relapse is the major determinant of poor outcomes in this population, de-escalation of chemotherapy intensity might be feasible in the 10% to 15% DS-ALL patients with ETV6-RUNX1 or high hyperdipoidy in whom TRM is the major limiting event. As infection-associated TRM occurs during all treatment phases, including the maintenance period, increased surveillance and supportive care is required throughout therapy. Improvement in outcome will require better understanding of the causes of treatment failure and TRM, incorporation of new therapies targeting the unique biological properties of DS-ALL, and enhanced supportive care measures to reduce the risk of infection-related TRM. To facilitate these goals, an international collaboration plans to establish a prospective DS-ALL registry and develop specific supportive care recommendations for this at-risk population.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".