Pre-pregnancy and pregnancy disorders, pre-term birth and the risk of cerebral palsy: a population-based study
Bibliographic record
Abstract
BACKGROUND: Cerebral palsy (CP) is the most common cause of childhood physical disability whose aetiology remains unclear in most cases. Maternal pre-existing and pregnancy complications are recognized risk factors of CP but the extent to which their effects are mediated by pre-term birth is unknown. METHODS: Population-based cohort study in Sweden including 2 055 378 singleton infants without congenital abnormalities, born between 1999 and 2019. Data on maternal and pregnancy characteristics and diagnoses of CP were obtained by individual record linkages of nationwide Swedish registries. Exposure was defined as maternal pre-pregnancy and pregnancy disorders. Inpatient and outpatient diagnoses were obtained for CP after 27 days of age. Adjusted rate ratios (aRRs) were calculated, along with 95% CIs. RESULTS: A total of 515 771 (25%) offspring were exposed to maternal pre-existing chronic disorders and 3472 children with CP were identified for a cumulative incidence of 1.7 per 1000 live births. After adjusting for potential confounders, maternal chronic cardiovascular or metabolic disorders, other chronic diseases, mental health disorders and early-pregnancy obesity were associated with 1.89-, 1.24-, 1.26- and 1.35-times higher risk (aRRs) of CP, respectively. Most notably, offspring exposed to maternal antepartum haemorrhage had a 6-fold elevated risk of CP (aRR 5.78, 95% CI, 5.00-6.68). Mediation analysis revealed that ∼50% of the effect of these associations was mediated by pre-term delivery; however, increased risks were also observed among term infants. CONCLUSIONS: Exposure to pre-existing maternal chronic disorders and pregnancy-related complications increases the risk of CP in offspring. Although most infants with CP were born at term, pre-term delivery explained 50% of the overall effect of pre-pregnancy and pregnancy disorders on CP.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".