The Relation between Maternal Schizophrenia and Low Birth Weight is Modified by Paternal Age
Bibliographic record
Abstract
OBJECTIVE: Paternal characteristics have never been considered in the relation between maternal schizophrenia and adverse pregnancy outcomes. The aim of our study was to consider different paternal ages while investigating the relation between maternal schizophrenia and low birth weight (LBW), using a nationwide population-based dataset. METHOD: Our study used data from the 2001 to 2003 Taiwan National Health Insurance Research Dataset and birth certificate registry. A total of 543 394 singleton live births were included. We performed multivariate logistic regression analyses to explore the relation between maternal schizophrenia and the risk of LBW, taking different paternal age groups into account (aged 29 years or younger, 30 to 39 years, and 40 years and older), and after adjusting for other characteristics of infant, mother, and father as well as the difference between the parent's ages. RESULTS: Mothers with schizophrenia had a higher percentage of LBW infants than mothers who did not (11.8%, compared with 6.8%). For infants whose mothers had schizophrenia, the adjusted odds ratios of LBW were 1.47 (95% CI 1.02 to 2.27, P < 0.05) and 2.80 (95% CI 1.42 to 5.51, P < 0.01) times greater than for infants whose mothers did not have schizophrenia, for paternal age groups of 30 to 39 years and 40 years or older, respectively. However, maternal schizophrenia was not a significant predictor of LBW for infants whose fathers were aged 29 years and younger. CONCLUSIONS: The relation between LBW and maternal schizophrenia is modified by paternal age. More attention should be paid to the interaction of paternal characteristics and maternal psychiatric disorders in producing adverse pregnancy outcomes.
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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 | 0.007 |
| 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 | 0.000 | 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".