Abstract 16828: Risk of Hematopoietic Cancer in Congenital Heart Diseased Children With or Without Genetic Syndromes
Bibliographic record
Abstract
Background: Individuals with genetic syndromes can manifest both congenital heart disease (CHD) and cancer due to possible common underlying pathways. However, reliable risk estimates of hematopoietic cancer (HC) among children with CHD based on large population-based data are scant. Method: We conducted a population-based analysis to estimate the cumulative incidence of HC in a cohort of children (0-18) born between 1999 and 2017 with at least one hospitalization records of CHD diagnosis or CHD-related procedural code. The data source was the Canadian Institute for Health Informatics-Discharge Abstract Database which regularly collects hospitalization and day surgery records in all Canadian provinces except Quebec, comprising over 30 million people. Hematopoietic cancer and syndromes were both identified by hospitalization diagnoses. We used modified Kaplan-Meier curve analysis to estimate the cumulative incidences [with 95% confidence intervals (CI)] up to 18 years of age, with death as a competing risk and stratified by the binary indicator of genetic syndrome status. Result: We followed 92815 CHD children from birth for 921,866 person-years. In this study population,10.5% had genetic syndromes and 461 cases of HC were observed, yielding incidence rates of HC 22.5 (95% CI: 19.4-25.4) and 3.0 (2.6-3.3) per 10,000 person-years for children with and without genetic syndrome, respectively. Cumulative incidence of HC up to age 18 was 2.6% (95% CI: 2.2-2.9%) among children with a genetic syndrome, and 0.40% (0.34-0.46%) without the syndrome. It was higher in the first 6 years of life (2.1%) than the subsequent 6-years intervals up to adulthood (0.4% in 6-12 years and 0.2% in 12-18 years of age). Conclusion: This is the first population-based analysis documenting that genetic syndromes in CHD children are a powerful predictor of hematopoietic cancers. The finding is important in informing risk-stratified policy recommendations to protect CHD children from cancer.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".