Varying Impact of Gestational Diabetes Mellitus on Incidence of Childhood Cancers: An Age-Stratified Retrospective Cohort Study
Bibliographic record
Abstract
Objective: We studied the association between gestational diabetes and early versus late childhood cancer. Research Design & Methods: We conducted a retrospective cohort study of 1 million children born between 2006 and 2019 in Quebec, Canada. We identified children who were exposed to gestational diabetes in utero and followed them from birth up to 14 years of age to identify new onset cancers. We estimated hazard ratios (HR) for the association between gestational diabetes and childhood cancer using Cox proportional regression models adjusted for covariates through inverse propensity score weights. Results: A total of 83,626 children (8.2%) were exposed to gestational diabetes and 1,702 developed cancer during 7.6 million person-years of follow-up. Children exposed to gestational diabetes had a higher risk of any cancer (HR 1.19, 95% CI 1.01-1.40), with signals present for blood cancer (HR 1.27, 95% CI 0.92-1.76) and solid tumors (HR 1.14, 95% CI 0.94-1.40). The association between gestational diabetes and cancer was strongest early in life and decreased with age. Gestational diabetes was associated with 1.47 times the risk of any cancer (95% CI 1.21-1.79), 1.44 times the risk of solid cancer (95% CI 1.12-1.87), and 1.61 times the risk of blood cancer (95% CI 1.09-2.36) in children under 2 years. Gestational diabetes was not significantly associated with blood or solid cancers after 2 years of age and all associations disappeared after 6 years. Conclusions: Hyperglycemia may be carcinogenic in utero and may be a novel risk factor for early childhood 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".