Neonatal Outcomes of Mothers With a Disability
Bibliographic record
Abstract
OBJECTIVES: To assess the risk of neonatal complications among women with a disability. METHODS: This population-based cohort study comprised all hospital singleton livebirths in Ontario, Canada from 2003 to 2018. Newborns of women with a physical (N = 144 187), sensory (N = 44 988), intellectual or developmental (N = 2207), or ≥2 disabilities (N = 8823) were each compared with 1 593 354 newborns of women without a disability. Outcomes were preterm birth <37 and <34 weeks, small for gestational age birth weight (SGA), large for gestational age birth weight, neonatal morbidity, and mortality, neonatal abstinence syndrome (NAS), and NICU admission. Relative risks were adjusted for social, health, and health care characteristics. RESULTS: Risks for neonatal complications were elevated among newborns of women with disabilities compared with those without disabilities. Adjusted relative risks were especially high for newborns of women with an intellectual or developmental disability, including preterm birth <37 weeks (1.37, 95% confidence interval 1.19-1.58), SGA (1.37, 1.24-1.59), neonatal morbidity (1.42, 1.27-1.60), NAS (1.53, 1.12-2.08), and NICU admission (1.53, 1.40-1.67). The same was seen for newborns of women with ≥2 disabilities, including preterm birth <37 weeks (1.48, 1.39-1.59), SGA (1.13, 1.07-1.20), neonatal morbidity (1.28, 1.20-1.36), NAS (1.87, 1.57-2.23), and NICU admission (1.35, 1.29-1.42). CONCLUSIONS: There is a mild to moderate elevated risk for complications among newborns of women with disabilities. These women may need adapted and enhanced preconception and prenatal care, and their newborns may require extra support after birth.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".