Perinatal Depression: A Guide to Detection and Management in Primary Care
Bibliographic record
Abstract
INTRODUCTION: Existing guidelines for primary care clinicians (PCCs) on the detection and management of perinatal depression (PD) contain important gaps. This review aims to provide PCCs with a summary of clinically relevant evidence in the field. METHODS: A narrative literature review was conducted by searching PubMed and PsycINFO for articles published between 2010 to 2023. Guidelines, systematic reviews, clinical trials, and/or observational studies were all examined. RESULTS: Screening with the Edinburgh Postnatal Depression Scale or Patient Health Questionnaire-9 followed by a diagnostic evaluation for major depressive disorder in probable cases can enhance PD detection. At-risk individuals and mild to moderate PD should be referred for cognitive behavioral therapy or interpersonal psychotherapy when available. Selective serotonin reuptake inhibitors should be used for moderate to severe PD, with sertraline, escitalopram, or citalopram being preferred first. Using paroxetine or clomipramine in pregnancy, and fluoxetine or doxepin during lactation is generally not preferred. Gestational antidepressant use is associated with a small increase in risk of reduced gestational age at birth, low birth weight, and lower APGAR scores, though whether these links are causal is unclear. Sertraline and paroxetine have the lowest rate of adverse events during lactation. Consequences of untreated PD can include maternal and offspring mortality, perinatal complications, poor maternal-infant attachment, child morbidity and maltreatment, less breastfeeding, and offspring developmental problems. CONCLUSIONS: These clinically relevant data can support the delivery of high-quality care by PCCs. Risks and benefits of PD treatments and the consequences of untreated PD should be discussed with patients to support informed decision making.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".