Preventing recurrence of postpartum depression by regulating sleep
Bibliographic record
Abstract
INTRODUCTION: Women are at a high risk of recurrence of depression in the postpartum period. Given the circumscribed duration of the risk period and knowledge of its triggers, postpartum depression should be easily preventable. However, prophylactic drug studies have reported contradictory findings partly due to the heterogeneity of the disorder. Currently, there are no studies on the efficacy of psychotherapy in the prevention of postpartum depression in women with major depressive or bipolar disorder. AREAS COVERED: This review evaluates the results of controlled medication and psychotherapeutic studies in the prevention of depression in women with major depressive disorder or bipolar disorder; it further suggests that the management of sleep loss/insomnia may be an effective strategy in the prevention of postpartum depression. EXPERT OPINION: A thorough understanding of the clinical course of the antecedent mood disorder and historical treatment response is necessary before the implementation of strategies for the prevention of postpartum depression. Targeting disturbed and/or insufficient sleep - a common and early transdiagnostic symptom of peripartum psychiatric disorders - may be a more effective intervention for the prevention of postpartum depression and psychiatric comorbidities in some individuals than the traditional approach of antidepressant use.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".