Predicting Anthracycline‐induced Cardiotoxicity in Children – Genome‐Wide Association Study
Bibliographic record
Abstract
Identified genetic markers for anthracycline‐induced cardiotoxicity (ACT) explain only a small fraction of the variability of this phenotype, suggesting the presence of other, as‐of‐yet unidentified susceptibility loci. Therefore, our goal is to identify additional genetic markers with large effect size via genome‐wide association study (GWAS). Patients were recruited and clinically characterized (age at start of treatment, cumulative dose, gender, anthracycline and tumor type, radiation therapy involving the heart, follow‐up time and assessment of LV dysfunction) via the Canadian Pharmacogenomics Network for Drug Safety. We have recruited and clinically characterized over 400 patients from across Canada to serve as our discovery cohort and over 120 patients from the Emma Children's Hospital in Amsterdam, the Netherlands, to be used as our replication cohort. More patients are currently being recruited and the clinical characterization is ongoing. We are now genotyping samples with an Illumina GWAS panel and the statistical analyses will be underway soon using SVS/Helix Tree, SPSS, Epi Info, PLINK and R. Novel genetic predictors for ACT may become essential for screening patients before the start of treatment, comprehensive risk assessment and management, and evidence‐based treatment decisions, monitoring and prevention. This work was supported by CIHR, CFRI, and Genome BC.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".