Multiple modifiable lifestyle factors and the risk of perinatal depression during pregnancy: Findings from the GUSTO cohort
Bibliographic record
Abstract
Studies have identified lifestyle risk factors for perinatal depression, but none have examined the cumulative effect of these risk factors in pregnant women. We considered the following six factors during pregnancy: poor diet quality (Healthy eating index for Singapore pregnant women 5), physical inactivity (<600 MET-minutes/week), vitamin D insufficiency (<50 nmol/l), smoking before or during pregnancy, and the perceived need for social support. Probable depression was assessed using the Edinburgh postnatal depression scale during pregnancy (>15) and at three months postpartum (≥13). Prevalence risk ratios were calculated with Poisson regressions while adjusting for potential confounders. Of 535 pregnant women, 207 (39%) had zero or one risk factor, 146 (27%) had two, 119 (22%) had three, 48 (9%) had four, and 15 (3%) had ≥5 risk factors at 26–28 weeks' gestation. These six lifestyle habits contributed to 32% of the variance in depressive symptoms during pregnancy. The prevalence of being probably depressed was 6.4 (95% CI 2.1, 19.8; ptrend < 0.001) for expecting women who had ≥4 risk factors compared to women who had ≤1 risk factor. No association was observed between the number of risk factors and depressive symptoms at 3 months postpartum (ptrend = 0.746). Pregnant women with ≥4 lifestyle risk factors showed a higher prevalence of depression during pregnancy, while no associations were observed for postpartum depression. This cohort is registered under the Clinical Trials identifier NCT01174875; http://www.clinicaltrials.gov/ct2/show/NCT01174875?term=GUSTO&rank=2
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".