Careful considerations for the treatment of posttraumatic stress disorder during and following pregnancy
Bibliographic record
Abstract
INTRODUCTION: The focus on perinatal mental health has expanded recently, though there is less research on post-traumatic stress disorder (PTSD). Therefore, a review of the literature was undertaken and coupled with expert clinical insights to discuss current clinical practice recommendations for PTSD in the perinatal period. AREAS COVERED: This review covers considerations for the assessment, prevention, and treatment of PTSD during the perinatal period. Within these sections, evidence-based and promising practices are outlined. Extra attention is afforded to treatment, which includes considerations from both psychotherapeutic and psychopharmacological perspectives. This review closes with coverage of three important and related areas of consideration, including bereavement, intimate partner violence, and childhood sexual abuse. EXPERT OPINION: Psychotherapeutic interventions for PTSD during pregnancy are limited, and no strong recommendations can be supported at this time while evidence points toward the effectiveness of cognitive behavioral therapies and eye movement desensitization therapy as first-line treatments postpartum though research evidence is also limited. Research on psychopharmacological interventions is similarly scarce, though selective serotonin reuptake inhibitors may be beneficial. Clinicians should also be mindful of additional considerations that may be needed for the treatment of PTSD in the context of bereavement, intimate partner violence, and history of sexual violence.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".