Maternal and offspring outcomes in women with intellectual and developmental disabilities: a population‐based cohort study
Bibliographic record
Abstract
OBJECTIVE: To compare the risks for adverse maternal and offspring outcomes in women with and without intellectual and developmental disabilities. DESIGN: Population-based cohort study. SETTING: Ontario, Canada. POPULATION: Singleton obstetrical deliveries to 18- to 49-year-old women with and without intellectual and developmental disabilities (n = 3932 in the exposed cohort, n = 382 774 in the unexposed cohort; 2002-2011 fiscal years). METHODS: Women with intellectual and developmental disabilities were identified based on diagnoses in health administrative data or receipt of disability income support. The unexposed cohort comprised women without intellectual and developmental disabilities. Modified Poisson regression was used to compute adjusted relative risks (aRR) and 95% confidence intervals (CI) comparing the two cohorts. MAIN OUTCOME MEASURES: Primary maternal outcomes were: gestational diabetes, gestational hypertension, pre-eclampsia, eclampsia, and venous thromboembolism. Primary offspring outcomes were: preterm birth, small for gestational age, and large for gestational age. RESULTS: The exposed cohort, compared with the unexposed cohort, had increased risks for pre-eclampsia (aRR 1.47, 95% CI 1.11-1.93) and venous thromboembolism (aRR 1.60, 95% CI 1.17-2.19). Their offspring had increased risks for preterm birth (aRR 1.63, 95% CI 1.47-1.80) and small for gestational age (aRR 1.35, 95% CI 1.25-1.45). CONCLUSIONS: These findings suggest that there is a need to address modifiable risk factors for adverse outcomes among women with intellectual and developmental disabilities prior to and during pregnancy. Moreover, there is a need to enhance monitoring for maternal and offspring complications in this population. TWEETABLE ABSTRACT: Large cohort study: intellectual and developmental disabilities predispose women/babies to adverse outcomes.
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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 | 0.002 |
| 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 | 0.001 |
| 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".