CanCHD Study of Hematopoietic Cancers in Children With and Without Genetic Syndromes
Bibliographic record
Abstract
BACKGROUND: Individuals with genetic syndromes can manifest both congenital heart disease (CHD) and cancer attributable to possible common underlying pathways. To date, reliable risk estimates of hematopoietic cancer (HC) among children with CHD based on large population-based data remain scant. This study sought to quantify the risk of HC by the presence of genetic syndrome among children with CHD. METHODS AND RESULTS: Data sources were the Canadian CHD database, a nationwide database on CHD (1999-2017), and the CCR (Canadian Cancer Registry). Standardized incidence ratios were calculated for comparing HC incidences in children with CHD with the general pediatric population. A modified Kaplan-Meier curve was used to estimate the cumulative incidence of HC with death as a competing risk. A total of 143 794 children (aged 0-17 years) with CHD were followed up from birth to age 18 years for 1 314 603 person-years. Of them, 8.6% had genetic syndromes, and 898 HC cases were observed. Children with known syndromes had a substantially higher risk of incident HC than the general pediatric population (standardized incidence ratio, 13.4 [95% CI, 11.7-15.1]). The cumulative incidence of HC was 2.44% (95% CI, 2.11-2.76) among children with a syndrome and 0.79% (95% CI, 0.72-0.87) among children without a syndrome. Acute myeloid leukemia had a higher cumulative incidence during early childhood than acute lymphoblastic leukemia. CONCLUSIONS: This is the first large population-based analysis documenting that known genetic syndromes in children with CHD are a significant predictor of HC. The finding could be essential in informing risk-stratified policy recommendations for cancer surveillance in children with CHD.
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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".