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Record W4384120373 · doi:10.1001/jamaoncol.2023.2163

Management of Down Syndrome–Associated Leukemias

2023· review· en· W4384120373 on OpenAlexaff
Anupam Verma, Philip J. Lupo, Nirali N. Shah, Johann Hitzler, Karen R. Rabin

Bibliographic record

VenueJAMA Oncology · 2023
Typereview
Languageen
FieldMedicine
TopicAcute Myeloid Leukemia Research
Canadian institutionsHospital for Sick Children
FundersNational Cancer InstituteNational Institutes of Health
KeywordsMedicineDown syndromeMyeloid leukemiaInternal medicineClinical trialLeukemiaPopulationPediatricsOncologyMyeloid

Abstract

fetched live from OpenAlex

Importance: Down syndrome (DS), caused by an extra copy of material from chromosome 21, is one of the most common genetic conditions. The increased risk of acute leukemia in DS (DS-AL) has been recognized for decades, consisting of an approximately 150-fold higher risk of acute myeloid leukemia (AML) before age 4 years, and a 10- to 20-fold higher risk of acute lymphoblastic leukemia (ALL), compared with children without DS. Observations: A recent National Institutes of Health-sponsored conference, ImpacT21, reviewed research and clinical trials in children, adolescents, and young adults (AYAs) with DS-AL and are presented herein, including presentation and treatment, clinical trial design, and ethical considerations for this unique population. Between 10% to 30% of infants with DS are diagnosed with transient abnormal myelopoiesis (TAM), which spontaneously regresses. After a latency period of up to 4 years, 20% to 30% develop myeloid leukemia associated with DS (ML-DS). Recent studies have characterized somatic mutations associated with progression from TAM to ML-DS, but predicting which patients will progress to ML-DS remains elusive. Clinical trials for DS-AL have aimed to reduce treatment-related mortality (TRM) and improve survival. Children with ML-DS have better outcomes compared with non-DS AML, but outcomes remain dismal in relapse. In contrast, patients with DS-ALL have inferior outcomes compared with those without DS, due to both higher TRM and relapse. Management of relapsed leukemia poses unique challenges owing to disease biology and increased vulnerability to toxic effects. Late effects in survivors of DS-AL are an important area in need of further study because they may demonstrate unique patterns in the setting of chronic medical conditions associated with DS. Conclusions and Relevance: Optimal management of DS-AL requires specific molecular testing, meticulous supportive care, and tailored therapy to reduce TRM while optimizing survival. There is no standard approach to treatment of relapsed disease. Future work should include identification of biomarkers predictive of toxic effects; enhanced clinical and scientific collaborations; promotion of access to novel agents through innovative clinical trial design; and dedicated studies of late effects of treatment.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.920
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0010.003

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.

Opus teacher head0.098
GPT teacher head0.424
Teacher spread0.325 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations24
Published2023
Admission routes1
Has abstractyes

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