Treatment of Perinatal Mood and Anxiety Disorders: A Review
Bibliographic record
Abstract
OBJECTIVES: To review the nonpharmacologic and pharmacologic treatment modalities for perinatal mood and anxiety disorders and to discuss the importance of weighing the risks and the benefits of exposing the fetus or baby to maternal mental illness as opposed to exposure to antidepressant medications. METHODS: We conducted a literature search of the PubMed and MEDLINE databases. Key words included the following: perinatal, pregnancy, postpartum, depression, anxiety, pharmacologic, nonpharmacologic, psychotherapy, and treatment. RESULTS: Recent literature reflects that both pharmacologic and nonpharmacologic treatments for perinatal women are associated with positive and negative outcomes. No treatment decision was found to be risk-free. The detrimental effects of untreated mental illness on the mother, as well as on the baby, highlight the need for treatment intervention. The long-term effects of exposure to either medications or maternal mental illness are unknown, as yet. CONCLUSION: Women with perinatal depression and anxiety disorders require timely and efficient management with a goal of providing symptom relief for the suffering mother while simultaneously ensuring the baby's safety. Although knowledge in the area of appropriate intervention is constantly evolving, rigorous and scientifically sound research in the future is critical.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| 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) | 0.003 | 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".