Mood instability, depression, and anxiety in pregnancy and adverse neonatal outcomes
Bibliographic record
Abstract
BACKGROUND: Antenatal women experience an increased level of mood and anxiety symptoms, which have negative effects on mothers' mental and physical health as well as the health of their newborns. The relation of maternal depression and anxiety in pregnancy with neonate outcomes is well-studied with inconsistent findings. However, the association between antenatal mood instability (MI) and neonatal outcomes has not been investigated even though antenatal women experience an elevated level of MI. We sought to address this gap and to contribute to the literature about pregnancy neonate outcomes by examining the relationship among antenatal MI, depression, and anxiety and neonatal outcomes. METHODS: A prospective cohort of women (n = 555) participated in this study at early pregnancy (T1, 17.4 ± 4.9 weeks) and late pregnancy (T2, 30.6 ± 2.7 weeks). The Edinburgh Postnatal Depression Scale (EPDS) was used to assess antenatal depressive symptoms, anxiety was measured by the EPDS anxiety subscale, and mood instability was measured by a visual analogue scale with five questions. These mood states together with stress, social support, as well as lifestyle were also examined in relation to neonatal outcomes using chi-square tests and logistic regression models. RESULTS: Mood instability, depression, and anxiety were unrelated to adverse neonatal outcomes. Only primiparous status was associated with small for gestational age after Bonferroni correction. CONCLUSIONS: We report no associations between antenatal mood symptoms including MI, depression, and anxiety and neonatal outcomes. More studies are required to further explore the relationship between antenatal mood instability, depression, and anxiety and neonatal 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".