Maternal anorexia nervosa and risk of mental and neurodevelopmental morbidity in offspring
Bibliographic record
Abstract
BACKGROUND: Anorexia nervosa has the potential to affect fetal neurodevelopment. We examined the association between maternal anorexia nervosa and mental, substance-related, and neurodevelopmental morbidity in offspring. METHODS: We conducted a retrospective cohort study of 1,269,370 children in Quebec, Canada, between 2006 and 2022. The main exposure was maternal anorexia nervosa requiring admission. The outcome was childhood hospitalization for mental, substance-related, or neurodevelopmental disorders between birth and age 17 years, with follow-up ending in 2023. We estimated hazard ratios (HR) and 95% confidence intervals (CI) for the association between maternal anorexia nervosa and child outcomes using Cox regression models adjusted for patient characteristics. RESULTS: In total, 2,447 (0.2%) children had a mother admitted for anorexia nervosa. Children of mothers with anorexia nervosa had higher hospitalization rates for mental, substance-related, and neurodevelopmental morbidity than other children (104.7 vs. 51.4 per 1,000 by age 17 years). Children whose mothers had anorexia nervosa were particularly at risk of mental health hospitalization (HR 2.30, 95% CI 1.52-3.49), especially for anorexia nervosa (HR 10.62, 95% CI 5.06-22.29) and anxiety and stress disorders (HR 1.84, 95% CI 1.09-3.08), compared with unexposed children. Maternal anorexia nervosa was associated with substance-related (HR 2.01, 95% CI 1.26-3.21) and neurodevelopmental morbidity in children (HR 1.55, 95% CI 1.19-2.01). CONCLUSION: Maternal anorexia nervosa is associated with childhood hospitalization for mental health, substance-related, and neurodevelopmental morbidity, although results should be interpreted with caution owing to potential confounders. Mothers with anorexia nervosa may benefit from psychosocial support to prevent mental and neurodevelopmental morbidity in offspring.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".