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Predicting valvular heart disease in adult survivors of childhood cancer: A report from the Childhood Cancer Survivor Study (CCSS) and St. Jude Lifetime Cohort (SJLIFE).

2025· article· en· W4410805477 on OpenAlexaff
Daniel A. Mulrooney, Qi Liu, Farideh Bagherzadeh‐Khiabani, James E. Bates, Suman Shrestha, Gregory T. Armstrong, Louis S. Constine, Kirsten K. Ness, Melissa M. Hudson, Yutaka Yasui, Rebecca M. Howell

Bibliographic record

VenueJournal of Clinical Oncology · 2025
Typearticle
Languageen
FieldMedicine
TopicChemotherapy-induced cardiotoxicity and mitigation
Canadian institutionsUniversity of Alberta
FundersNational Institutes of HealthAmerican Lebanese Syrian Associated Charities
KeywordsMedicineChildhood cancerCohortCancerDiseaseCohort studyPediatricsInternal medicineGerontologyOncology

Abstract

fetched live from OpenAlex

10070 Background: Radiotherapy (RT)-related valvular heart disease (VHD) is an understudied late toxicity of childhood cancer therapy. We aimed to define the risk of VHD with clinical data available at 5 and 20 years from cancer diagnosis. Methods: Mean heart RT doses were estimated for participants of the CCSS and SJLIFE cohorts treated with RT. Two piecewise exponential regression prediction models were developed in the CCSS, from entry into survivorship (5 years post cancer diagnosis) and 20 years post diagnosis (inclusive of age- and lifestyle-acquired risk factors), to assess subsequent risk of developing severe/life-threatening/fatal VHD (≥ grade 3 Common Terminology Criteria for Adverse Events [CTCAE]) by age 50 years. Models were validated among clinically assessed SJLIFE survivors. Results: Among 18,807 CCSS participants [mean age (±standard deviation) at diagnosis = 8.1 (5.8) years and 40 (11.1) at assessment] including 9,998 treated with RT, 164 (0.9%) reported VHD after cohort entry. Of those ≥20 years post diagnosis (n = 16,618) [7.9 (5.8) years at diagnosis; 42.5 (9.6) at assessment] 138 (0.8%) reported VHD. In SJLIFE, 44 (1.0%) of 4,388 survivors, including 2,103 treated with RT, and 35 (1.4%) of 2,423 ≥20-year survivors had VHD (mean ages at diagnosis and assessment: 7.8 [5.7] and 32 [12] years; 7.6 (5.5) and 38.7 (9.2) years, respectively). Prediction performance at age 50 years was good for both models [areas under the receiver operating characteristic curves 0.84 (95% CI 0.79-0.89) and 0.87 (95% CI 0.81-0.91)]. For each 10 Gy of heart RT, the rate of VHD increased approximately 2.5-fold (Table). Acquired risk factors, except glucose intolerance, further increased the risk, marginally for hypertension, significantly (p < 0.05) for obesity (RR 1.7 95% CI 1.0-2.8) and dyslipidemia (RR 2.3 95% CI 1.3-4.0). Conclusions: In the first study to develop validated risk prediction models for VHD in survivors of childhood cancer, mean heart RT dose and acquired factors significantly increased the risk, suggesting opportunities for intervention. Rate ratios (RR) of VHD. From entry into survivorship From 20-year post diagnosis RR (95% CI) RR (95% CI) Mean heart RT dose (per 10 Gy) 2.4 (2.2-2.7) 2.5 (2.2-2.9) Age at diagnosis (years) <5 referent referent 5-9 1.1 (0.6-2.1) 1.2 (0.6-2.5) 10-15 1.1 (0.6-2.1) 1.3 (0.6-2.6) ≥15 1.1 (0.6-2.1) 1.2 (0.6-2.6) Female sex 1.1 (0.8-1.5) 1.3 (0.9-1.9) Race/Ethnicity non-Hispanic White referent referent non-Hispanic Black 1.3 (0.5-2.8) 0.8 (0.2-2.3) Other 1.1 (0.6-1.6) 1.0 (0.6-1.7) Anthracycline dose (mg/m 2 ) None referent referent <100 0.6 0.1-1.6 0.8 (0.2-2.2) 100-249 0.9 0.5-1.4 0.9 (0.5-1.5) ≥250 1.5 1.0-2.2 1.3 (0.8-2.1) Acquired risk factors * Glucose intolerance N/A

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)0.002
metaresearch head score (Gemma)0.003
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 score0.028
Threshold uncertainty score0.056

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.027
GPT teacher head0.402
Teacher spread0.375 · 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".

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Citations0
Published2025
Admission routes1
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