Occurrence of postpartum manic or mixed episodes in women with bipolar I disorder: A systematic review and meta‐analysis
Bibliographic record
Abstract
OBJECTIVE: Accurate information on the frequency and prevalence of manic or mixed episodes is important for therapeutic, prognostic, and safety concerns. We aimed to estimate the risk of relapse of manic and mixed episodes after delivery in women with bipolar I disorder or schizoaffective disorder-bipolar type. METHODS: Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a comprehensive literature search in PubMed, PsycINFO, Embase, and Cochrane databases was carried out on November 17, 2022, using the terms ((bipolar disorder) OR (manic depressive illness)) AND (mania)) AND (postpartum)) AND (recurrence)) AND (relapse). The search was updated on March 29, 2023. Case studies and qualitative analyses were excluded. Twelve studies reporting on 3595 deliveries in 2183 women were included in the quantitative analysis. RESULTS: = 91.06%). Using data from the nine studies that reported the percentage of medication use during pregnancy, we estimated a meta-regression model with the percent medication use as a continuous explanatory variable. The estimated prevalence of relapse was 58.1% (95% CI, 9.6 to 39.3 to 76.8) for studies with no medication use and 25.9% (95% CI, 10.5-41.3) for studies with 100% medication use. The difference between the two prevalence estimates was statistically significant, z = -2.099, p = 0.0359. CONCLUSIONS: Our findings suggest an overall pooled estimate of postpartum relapse risk of 39%, while the pooled estimate of risk for manic and mixed episodes was 38%. These findings highlight the need to educate patients with bipolar I disorder, and their healthcare professionals about the high risk of relapse of manic or mixed episodes after delivery.
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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 | 0.017 | 0.038 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.043 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".