Longitudinal risk of maternal hospitalization for mental illness following preterm birth
Bibliographic record
Abstract
BACKGROUND: Preterm birth may affect maternal mental health, yet most studies focus on postpartum mental disorders only. We explored the relationship between preterm delivery and the long-term risk of maternal hospitalization for mental illness after pregnancy. METHODS: We performed a longitudinal cohort study of 1,381,300 women who delivered between 1989 and 2021 in Quebec, Canada, and had no prior history of mental illness. The exposure was preterm birth, including extreme (<28 weeks), very (28-31 weeks), and moderate to late (32-36 weeks). The outcome was subsequent maternal hospitalization for depression, bipolar, psychotic, stress and anxiety, personality disorders, and self-harm up to 32 years later. We used adjusted Cox proportional hazards models to estimate hazard ratios (HR) and 95% confidence intervals (CI) for the association between preterm birth and mental illness hospitalization. RESULTS: Compared with term, women who delivered preterm had a higher rate of mental illness hospitalization (3.81 vs. 3.01 per 1000 person-years). Preterm birth was associated with any mental illness (HR 1.38, 95% CI 1.35-1.41), including depression (HR 1.37, 95% CI 1.32-1.41), psychotic disorders (HR 1.35, 95% CI 1.25-1.44), and stress and anxiety disorders (HR 1.42, 95% CI 1.38-1.46). Delivery at any preterm gestational age was associated with the risk of mental hospitalization, but risks were greatest around 34 weeks of gestation. Preterm birth was strongly associated with mental illness hospitalization within 2 years of pregnancy, although associations persisted throughout follow-up. CONCLUSIONS: Women who deliver preterm may be at risk of mental disorders in the short and long term.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".