Risk factors for peripartum depression in women with multiple sclerosis
Bibliographic record
Abstract
BACKGROUND: Peripartum depression (PPD) is underexplored in multiple sclerosis (MS). OBJECTIVE: To evaluate prevalence of and risk factors for PPD in women with MS. METHODS: Retrospective single-center analysis of women with MS with a live birth. Prevalence of PPD was estimated with logistic regression with generalized estimating equations (GEE). GEE evaluated predictors of PPD (e.g. age, marital status, parity, pre-pregnancy depression/anxiety, antidepressant discontinuation, sleep disturbance, breastfeeding, relapses, gadolinium-enhancing lesions, and disability). Factors significant in univariable analyses were included in multivariable analysis. RESULTS: We identified 143 live births in 111 women (mean age 33.1 ± 4.7 years). PPD was found in 18/143 pregnancies (12.6%, 95% CI = 7.3-17.8). Factors associated with PPD included older age (OR 1.16, 95% CI = 1.03-1.32 for 1-year increase), primiparity (OR 4.02, CI = 1.14-14.23), pre-pregnancy depression (OR 3.70, CI = 1.27-10.01), sleep disturbance (OR 3.23, CI = 1.17-8.91), and breastfeeding difficulty (OR 3.58, CI = 1.27-10.08). Maternal age (OR 1.17, CI = 1.02-1.34), primiparity (OR 8.10, CI = 1.38-47.40), and pre-pregnancy depression (OR 3.89, CI = 1.04-14.60) remained significant in multivariable analyses. Relapses, MRI activity, and disability were not associated with PPD. CONCLUSION: The prevalence of PPD in MS appeared similar to the general population, but was likely underestimated due to lack of screening. PPD can affect MS self-management and offspring development, and prospective studies are needed.
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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 | 0.003 |
| 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 | 0.001 |
| 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".