Risk of congenital anomalies in children who have a sibling with cancer: A matched cohort study
Bibliographic record
Abstract
PURPOSE: We assessed the risk of congenital anomalies in children who have a sibling with cancer. METHODS: We performed a matched cohort study of children born between 2006 and 2022 in Quebec. The exposure was having a sibling with cancer. Exposed children were matched to unexposed children based on sex, number of siblings, birth order, and year. The outcome included heart defects, orofacial clefts, and other anomalies. Using conditional logistic regression, we estimated odds ratios (OR) and 95 % confidence intervals (CI) for the association between having a sibling with cancer and the likelihood of having a congenital anomaly. RESULTS: A total of 2403 children who had a sibling with cancer were matched to 240,257 unexposed children. Congenital anomalies were more frequent in children who had a sibling with cancer compared with unexposed children (10.3 % vs 8.9 %). Overall, having a sibling with cancer was only weakly associated with congenital anomalies (OR 1.18, 95 % CI 1.04-1.35). Exposed children tended to have greater odds of polydactyly/syndactyly (OR 1.89, 95 % CI 1.11-3.21) and urinary defects (OR 1.50, 95 % CI 1.09-2.08) compared with unexposed children. CONCLUSIONS: Children who have a sibling with cancer have an only weakly elevated risk of congenital anomalies.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".