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Record W4384120739 · doi:10.3324/haematol.2023.283225

Down syndrome and leukemia: from basic mechanisms to clinical advances

2023· review· en· W4384120739 on OpenAlexaff
André Baruchel, Jean‐Pierre Bourquin, John D. Crispino, Sergi Cuartero, Henrik Hasle, Johann Hitzler, Jan‐Henning Klusmann, Shai Izraeli, Andrew A. Lane, Sébastien Malinge, Karen R. Rabin, Irene Roberts, Sandra Ryeom, Sarah K. Tasian, Elvin Wagenblast

Bibliographic record

VenueHaematologica · 2023
Typereview
Languageen
FieldMedicine
TopicAcute Myeloid Leukemia Research
Canadian institutionsHospital for Sick Children
FundersNational Cancer InstituteCancer Council Western AustraliaNational Institutes of HealthMinisterio de Ciencia e InnovaciónBundesministerium für Bildung und ForschungDeutsche ForschungsgemeinschaftEuropean CommissionSt. Jude Children's Research HospitalChildren's Hospital of PhiladelphiaAmerican Lebanese Syrian Associated CharitiesU.S. Department of Defense
KeywordsDown syndromeTrisomyMyeloid leukemiaLeukemiaMedicineAcute leukemiaPopulationLymphoblastic LeukemiaImmunologyBioinformaticsOncologyBiologyGeneticsPsychiatry

Abstract

fetched live from OpenAlex

Children with Down syndrome (DS, trisomy 21) are at a significantly higher risk of developing acute leukemia compared to the overall population. Many studies investigating the link between trisomy 21 and leukemia initiation and progression have been conducted over the last two decades. Despite improved treatment regimens and significant progress in iden - tifying genes on chromosome 21 and the mechanisms by which they drive leukemogenesis, there is still much that is unknown. A focused group of scientists and clinicians with expertise in leukemia and DS met in October 2022 at the Jérôme Lejeune Foundation in Paris, France for the 1st International Symposium on Down Syndrome and Leukemia. This meeting was held to discuss the most recent advances in treatment regimens and the biology underlying the initiation, progression, and relapse of acute lymphoblastic leukemia and acute myeloid leukemia in children with DS. This review provides a summary of what is known in the field, challenges in the management of DS patients with leukemia, and key questions in the field.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.961
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0060.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.006

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 head0.146
GPT teacher head0.439
Teacher spread0.293 · 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

Citations52
Published2023
Admission routes1
Has abstractyes

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