Bibliographic record
Abstract
OBJECTIVE: Postpartum nonpsychotic conditions are routinely treated with antidepressant therapy. However, a subset of this population with comorbid obsessive-compulsive disorder (OCD) is treatment-resistant. Optimal response is obtained by augmentation therapy with novel antipsychotics. The objective of this open-label study was to evaluate clinical response to quetiapine augmentation of SSRIs or SNRIs in treatment-resistant OCD in the postpartum. METHODS: Twenty-two postpartum women diagnosed with OCD as per DSM-IV criteria, who did not respond to at least 8 weeks of SSRI or SNRI monotherapy, were offered a trial of quetiapine augmentation for 12 weeks. Response (defined as >50% reduction in scores) was assessed using the Yale Brown Obsessive-Compulsive Scale (YBOCS) and Clinical Global Impressions scale (CGI). RESULTS: Seventeen patients agreed to a trial of quetiapine augmentation. Three withdrew early due to side effects, and 14 completed the 12-week trial. Of these, 11 responded to treatment within 12 weeks, with a mean (SD) response time of 5.9 (2.6) weeks. The mean (SD) baseline YBOCS score of 24.7 (6.8) dropped to a mean of 10.3 (9.0), with a mean reduction of 59.6%. Mean CGI scores at outcome were 1.9 (1.2). The average dose of response was 112.5 mg (76.4 mg). Sedation was the most commonly reported side effect. CONCLUSIONS: Although limited by lack of controls, this is the first study in a postpartum population where the addition of quetiapine to antidepressant therapy has been shown to be effective for treatment-refractory OCD. Quetiapine deserves further controlled study in this context.
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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) | 0.000 | 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) | 0.001 | 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 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".