Diagnosis, Pathophysiology, and Management of Mood Disorders in Pregnant and Postpartum Women
Bibliographic record
Abstract
Mood disorders disproportionately affect women across the lifespan. Mood disorders in pregnancy and the postpartum period are common and have profound implications for women and their children. These include obstetric and neonatal complications, impaired mother-infant interactions, and, at the extreme, maternal suicide and infanticide. Because obstetrician-gynecologists are often the first (and sometimes the only) point of contact for young women in the health care system, familiarity with the presentation and treatment of depressive illness in the perinatal period is imperative. The goal of this review is to synthesize essential information on depressive illness in the perinatal period with a focus on its most common and severe presentations, major depressive disorder and bipolar disorder. Accurate diagnosis of unipolar major depressive disorder from bipolar disorder can facilitate the selection of the best possible treatment alternatives. Counseling may be sufficient for perinatal women who have mild to moderate depression, but women who are severely depressed are likely to require antidepressant treatment. Women with bipolar disorder are at high risk for relapse if mood stabilizer medication is discontinued, and they are vulnerable to relapse near the time of delivery. Comanagement of their care with psychiatrists will increase their chances of avoiding a recurrence of illness.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".