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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".