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Overweight or Obesity and Outcomes in Children With Acute Lymphoblastic Leukemia

2025· article· en· W4410362282 on OpenAlexaff
Elena J. Ladas, Haiyang Sheng, Uma H. Athale, Barbara L. Asselin, Luis A. Clavell, Peter D. Cole, Yael Flamand, Jean-Marie Leclerc, Caroline Laverdière, Bruno Michon, Stephen E. Sallan, Lewis B. Silverman, Jennifer Welch, Song Yao, Kara M. Kelly

Bibliographic record

VenueJAMA Network Open · 2025
Typearticle
Languageen
FieldMedicine
TopicChildhood Cancer Survivors' Quality of Life
Canadian institutionsCentre Hospitalier Universitaire Sainte-JustineHamilton Health SciencesCentre hospitalier universitaire de QuébecUniversité de MontréalMcMaster Children's Hospital
Fundersnot available
KeywordsOverweightMedicineObesityBody mass indexCumulative incidencePediatricsChildhood obesityCohortCohort studyIncidence (geometry)Prospective cohort studyInternal medicine

Abstract

fetched live from OpenAlex

Importance: There are conflicting data on the association of overweight or obesity with clinical outcomes in childhood acute lymphoblastic leukemia (ALL). The duration of exposure to overweight or obesity may be a better indicator of the risk of poorer outcomes. Objective: To determine the association of the duration of overweight or obesity with treatment-related toxic effects, minimal residual disease, relapse, and survival in childhood ALL. Design, Setting, and Participants: In this prospective cohort study, fluctuations in z scores of body mass index (BMI) for age from diagnosis to the end of treatment (EOT) were examined in 794 children registered on a Dana Farber Cancer Institute ALL Consortium protocol from May 31, 2005, to December 15, 2011. Height and weight were abstracted from the medical record for classification of BMI z scores at diagnosis through EOT and into survivorship. Data were analyzed from July 1 to 31, 2024. Main Outcomes and Measures: The duration of overweight or obesity was defined as having overweight or obesity at 2 or more time points and compared with having overweight or obesity at no more than 1 time point. Kaplan-Meier survival curves were generated to examine association of overweight or obesity with overall survival (OS), event-free survival (EFS), and cumulative incidence of relapse. Results: Among the 794 patients included in the analysis, the mean age at diagnosis was 6.7 (range, 1.0-17.9) years, with 441 (55.5%) being male, 136 (17.1%) Hispanic, and 553 (69.6%) non-Hispanic. The prevalence of overweight or obesity increased from 234 of 793 (29.5%) at diagnosis to 346 of 715 (48.4%) by EOT. Having overweight or obesity at baseline or developing overweight or obesity during induction was not associated with treatment-related toxic effects or higher minimal residual disease. Children with overweight or obesity at 2 or more time points experienced inferior OS (3-year OS, 93.8% vs 98.0%; P = .01), increased relapse (3-year relapse rate, 10.5% vs 5.8%; P = .02), and lower EFS (3-year EFS, 89.0% vs 93.7%; P = .02), compared with children with overweight or obesity at no more than 1 time point. Multivariable Cox proportional hazards regression models revealed an association between increased risk of death (hazard ratio [HR], 3.49; 95% CI, 1.28-9.51; P = .01) and relapse (HR, 1.92; 95% CI, 1.07-3.46; P = .03) among children with overweight or obesity at 2 or more time points. Conclusions and Relevance: In this prospective cohort study of children with ALL, longer duration of overweight or obesity was associated with lower OS and EFS and higher rates of relapse, underscoring the need for interventions targeting overweight or obesity during treatment of children with ALL.

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)0.004
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 score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.012
GPT teacher head0.296
Teacher spread0.284 · 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

Citations9
Published2025
Admission routes1
Has abstractyes

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