In utero Exposure to Maternal Diabetes and the Risk of Cerebral Palsy: A Population-based Cohort Study
Bibliographic record
Abstract
BACKGROUND: Evidence on the effects of in utero exposure to maternal diabetes on cerebral palsy (CP) in offspring is limited. We aimed to examine the effects of pregestational (PGDM) and gestational diabetes (GDM) separately on CP risk and the mediating role of increased fetal size. METHODS: In a population-based study, we included all live births in Ontario, Canada, between 2002 and 2017 followed up through 2018 (n = 2,110,177). Using administrative health data, we estimated crude and adjusted associations between PGDM or GDM and CP using Cox proportional hazards models to account for unequal follow-up in children. For the mediation analysis, we used marginal structural models to estimate the controlled direct effect of PGDM (and GDM) on the risk of CP not mediated by large-for-gestational age (LGA). RESULTS: During the study period, 5,317 children were diagnosed with CP (187 exposed to PGDM and 171 exposed to GDM). Children of mothers with PGDM showed an increased risk (hazard ratio [HR]: 1.84 [95% confidence interval (CI): 1.59, 2.14]) after adjusting for maternal sociodemographic and clinical factors. We found no associations between GDM and CP (adjusted HR: 0.91 [0.77, 1.06]). Our mediation analysis estimated that LGA explained 14% of the PDGM-CP association. CONCLUSIONS: In this population-based birth cohort study, maternal pregestational diabetes was associated with increased risk of CP, and the increased risk was not substantially mediated by the increased fetal size.
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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 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".