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Record W4417130084 · doi:10.1182/blood.2025030898

Increased prevalence of clonal hematopoiesis in children with sickle cell disease

2025· article· en· W4417130084 on OpenAlexaff
Jessica Ulloa, Kristin Wuichet, Sara R. Rashkin, Yash Pershad, Caitlyn Vlasschaert, Mark Rodeghier, Yu Yao, Victor R. Gordeuk, Binal N. Shah, Clifford M. Takemoto, Santosh L. Saraf, Michael R. DeBaun, Mitchell J. Weiss, Alexander G. Bick

Bibliographic record

VenueBlood · 2025
Typearticle
Languageen
FieldMedicine
TopicHemoglobinopathies and Related Disorders
Canadian institutionsQueen's University
Fundersnot available
KeywordsSickle cell anemiaDiseaseCellAlleleHaematopoiesisAllele frequency

Abstract

fetched live from OpenAlex

ABSTRACT: Recent studies have reached opposing conclusions about whether clonal hematopoiesis (CH) is increased or decreased in patients with sickle cell disease (SCD). Given that CH is typically age-related, its presence in children with SCD could offer unique insights into early-life mutagenesis and disease-related stressors. We tested the primary and secondary hypotheses that children with SCD would have a higher prevalence of CH than age-, sex-, and race-matched children without SCD and that children with hydroxyurea would have a higher CH prevalence than children not treated with hydroxyurea. To address this, we conducted a cross-sectional study in 2 independent cohorts of children aged 0 to 18 years with SCD (N = 1025 and N = 1293, respectively) and a 2957-person matched comparison group. Using a highly sensitive, error-corrected sequencing assay capable of detecting CH at a variant allele frequency of ≥0.5%, we found that children with SCD have a significantly higher prevalence of CH than the comparison group (odds ratio [OR], 4.2; P = 7.4 × 10-13). In addition, CH was not associated with exposure to hydroxyurea therapy (OR, 0.76; P = .44).

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.003
GPT teacher head0.202
Teacher spread0.200 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations2
Published2025
Admission routes1
Has abstractyes

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