Congenital anomaly and childhood cancer: A population‐based, record linkage study
Bibliographic record
Abstract
BACKGROUND: The cause of the majority of childhood malignancies is unknown. Association with the presence of congenital anomalies has been noted in some studies. In this study, we describe and quantify the association between congenital anomalies and childhood cancer. PROCEDURE: Cases from the Northern Congenital Abnormality Survey (NorCAS) and the Northern Region Young Persons Malignant Disease Registry (NRYPMDR) were matched using four key variables: surname at birth, date of birth, postcode at birth, and infant gender. All potential matches were checked manually. A second match was performed for children with matched birth dates, postcodes and gender but different surnames, to identify children whose surname at diagnosis was not the same as at birth. RESULTS: Thirty-nine children with a congenital anomaly and a diagnosis of cancer were identified from a cohort of 599,290 children born during 1985-2001. There was an almost threefold overall increased risk of malignancy among these children (RR = 2.9; 95% CI 2.1, 3.9), particularly of acute lymphoblastic leukaemia (RR = 2.7; 95% CI 1.5, 5.0), acute myeloblastic leukaemia (RR = 22.0; 95% CI 12.1, 40.0), other leukaemia (RR = 7.5; 95% CI 1.3, 43.6) and lymphomas (RR = 5.3; 95% CI 2.4, 12.0). This increased rate remained when children with Down syndrome were excluded (RR = 1.8, 95% CI 1.2, 2.7). CONCLUSION: Our findings suggest that children born with congenital anomalies are at increased risk of certain types of cancer. Further linkage studies between high quality registers, may provide insights into the origins of such malignancies.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".