Prenatal Care and Adverse Pregnancy Outcomes among Women with Depression: A Nationwide Population-Based Study
Bibliographic record
Abstract
OBJECTIVE: To evaluate the quantity of prenatal care as a risk factor for giving birth to low birth weight (LBW), preterm, and small for gestational age (SGA) infants in a sample of women diagnosed with depressive disorder. METHOD: Our study used a population-based dataset, Taiwan's National Health Insurance Research Database, which we linked to Taiwan's birth certificate registry to identify a total of 5283 new mothers with depressive disorder. Multivariate logistic regression analyses were performed to measure the risk of giving birth to LBW, preterm, and SGA infants, relating to the number of prenatal care visits (10 or more, 8 to 9, and 7 or less) made by mothers with depressive disorder. RESULTS: After adjusting for a woman's age, monthly income, urbanization level of place of residence, geographic location, marital status, substance abuse, arterial hypertension, diabetes, anemia, coronary heart disease, malpresentation, insufficient or excessive fetal growth, placenta or previa abruption, and infant's sex and parity, regression analyses revealed that mothers with a history of depressive disorder who received prenatal care 7 times or less were 4.21 (95% CI 3.34 to 5.32, P < 0.001), 5.37 (95% CI 4.33 to 6.67, P < 0.001), and 2.41 (95% CI 2.03 to 2.86, P < 0.001) times as likely to have LBW, preterm, and SGA babies, respectively, compared with mothers with depressive disorder who received prenatal care visits 10 times or more. CONCLUSIONS: Mothers with a history of depressive disorder who make fewer prenatal care visits were at an increased risk of LBW, SGA, and preterm birth, compared with women with a history of depressive disorder who made an adequate number of prenatal visits.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".