Managing Bipolar Disorder during Pregnancy: Weighing the Risks and Benefits
Bibliographic record
Abstract
Background: Challenges for the clinical management of bipolar disorder (BD) during pregnancy are multiple and complex and include competing risks to mother and offspring. Method: We reviewed recent research findings on the course of BD during pregnancy and postpartum, as well as reproductive safety data on the major mood stabilizers. Results: Pregnancy, and especially the postpartum period, are associated with a high risk for recurrence of BD. This risk appears to be limited by mood-stabilizing treatments and markedly increased by the abrupt discontinuation of such treatments. However, drugs used to treat or protect against recurrences of BD vary markedly in teratogenic potential: there are low risks with typical neuroleptics, moderate risks with lithium, higher risks with older anticonvulsants such as valproic acid and carbamazepine, and virtually unknown risks with other newer-generation anticonvulsants and atypical antipsychotics (ATPs). Conclusions: Clinical management of BD through pregnancy and postpartum calls for balanced assessments of maternal and fetal risks and benefits. Contexte: Les problèmes liés au traitement clinique du trouble bipolaire (TB) durant la grossesse sont profonds et peuvent entraîner des risques concurrents pour la mère et l'enfant. Méthode: Nous avons examiné les résultats récents de la recherche sur le cours du TB durant la grossesse et le post-partum, de même que les données d'innocuité reproductrice des principaux régulateurs de l'humeur. Résultats: La grossesse et surtout la période du post-partum comportent des risques élevés de récurrence du TB. Les risques semblent être limités par les traitements aux régulateurs de l'humeur et notablement accrus par la cessation abrupte de ces traitements. Toutefois, les médicaments utilisés pour traiter ou prévenir les récurrences du TB varient beaucoup en ce qui concerne les risques tératogènes: les risques sont faibles pour les neuroleptiques typiques, modérés pour le lithium, élevés pour les anciens anticonvulsivants comme l'acide valproïque et la carbamazépine, et presque inconnus pour les autres anticonvulsivants de la nouvelle génération et les antipsychotiques atypiques (APA). Conclusions: Le traitement clinique des femmes souffrant du TB durant la grossesse et le post-partum demande des évaluations équilibrées des risques et des avantages pour la mère et le foetus.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".