Risk factors for incident peripartum mental illness in multiple sclerosis
Bibliographic record
Abstract
BACKGROUND: Mothers with MS face an increased incidence and prevalence of peripartum mental illness as compared to mothers without MS. OBJECTIVE: To determine the factors associated with the risk of peripartum mental illness among mothers with MS. METHODS: We identified mothers with MS with live births between 2002 and 2019 using linked population-based administrative data from Ontario, Canada. Using validated definitions, we estimated the incidence of mental illness (depression, anxiety, bipolar disorder) from conception through the first post-partum year (peripartum period). We used multivariable Poisson regression to assess the association between age, delivery year, area-level deprivation (Ontario Marginalization Index), disease duration, disability, and comorbidity and incidence of peripartum mental illness. RESULTS: Among 1745 mothers with MS, the mean (SD) age at conception was 31.2 (4.8) years. Mothers living in communities that lacked cohesion had increased rates of peripartum depression (incidence rate ratio [IRR] 1.25; 1.11-1.42) and anxiety (IRR 1.20; 1.07-1.33). Elevated MS disability level was associated with elevated peripartum depression rates (IRR 1.51; 1.12-2.04). CONCLUSION: Higher area-level deprivation and disability levels are associated with an increased incidence of peripartum mental illness. These findings may assist clinicians in identifying women with MS who may benefit from peripartum mental health support.
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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 | 0.001 |
| 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) | 0.002 | 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".