30-Second Takeaway
- MBCT reduces depressive symptoms versus treatment as usual in difficult-to-treat depression with sustained medium-term benefit.
- In youths on SSRI, atypical antipsychotic augmentation showed higher hazards for hospitalization and suicide-related outcomes versus bupropion.
- Fluoxetine plus CBT outperforms fluoxetine alone for adolescent depression with suicidal risk (improved response, lower 1-year recurrence).
Week ending June 13, 2026
Concise evidence brief: MBCT for difficult depression; pediatric SSRI augmentation risks; bipolarity influences prescribing; model guidance; adolescent suicide interventions
MBCT reduces symptoms versus treatment-as-usual in difficult-to-treat depression
In adults with current major depressive disorder meeting non-response, resistance, or chronic criteria, MBCT reduced depressive symptoms versus TAU at post-treatment (pooled SMD = -0.40). Benefits persisted at medium-term follow-up (pooled SMD = -0.41) with high posterior probability of surpassing a minimal important difference. No evidence of MBCT superiority over other active psychosocial interventions was found. Moderator analyses showed consistent effects across baseline severity, chronicity, and comorbidity, supporting broad applicability.
Higher Bipolarity Index associated with mood stabilizer/SGA prescribing in stable MDD outpatients
In 103 long-term stable outpatients with MDD, the Bipolarity Index was higher in patients receiving MS/SGA versus antidepressant monotherapy (24.1 vs 13.3; p<0.001). ROC analysis suggested a BI cutoff of 16 (AUC 0.765) with 69.4% sensitivity and 77.8% specificity for distinguishing MS/SGA prescribing. This cross-sectional association reflects prescribing patterns and does not establish treatment superiority or predictive validity for outcomes.
Methodological guidance for clinical prediction models in mental health
This guidance reviews development and validation steps for mental health prediction models, illustrated using data from 5,372 pregnant women. Authors emphasize avoiding overfitting, performing external validation, and integrating clinical expertise to improve generalizability. They compare regression and machine-learning approaches, highlighting trade-offs between interpretability and predictive performance.
References
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Additional Reads
Optional additional studies from this edition.