AUGMENTATION STRATEGIES FOR TREATMENT-RESISTANT ANXIETY DISORDERS: A SYSTEMATIC REVIEW AND META-ANALYSIS
Bibliographic record
Abstract
BACKGROUND: A systematic review and meta-analysis was conducted to explore the efficacy of medication augmentation strategies compared to control treatments in patients who have had a partial or no response to initial treatment for generalized anxiety disorder, social anxiety disorder, and panic disorder. METHODS: Double-blind controlled trials of medication augmentation in adult treatment-resistant anxiety disorders conducted between January 1990 and January 2015 were systematically reviewed and evaluated by two independent raters. The search identified 625 articles; 610 were excluded following abstract review and 15 had full-text screening. Studies had to include a definition of treatment resistance, exclude concomitant medications, and have a parallel or crossover design. Data extraction forms were completed in duplicate. RESULTS: Six studies were included in the meta-analysis. Effect estimates were calculated using random effects modeling; heterogeneity was assessed and subgroup and sensitivity analyses were completed. Primary outcome was response, defined by Clinical Global Impression-Improvement score of ≤2. Augmentation was not associated with an increased risk of response, as compared with placebo (RR = 1.08, 95% CI = 0.94-1.24). A small significant effect was found in reduction in symptom severity: standard mean difference = -0.32, 95% CI = -0.56 to -0.08. No significant differences between augmentation with medication versus placebo were found in ratings of functional impairment and dropouts due to adverse events. CONCLUSIONS: Augmentation does not appear to be beneficial in treatment-resistant anxiety disorders. These results may be limited by small study samples, and a small number of overall studies in the analysis.
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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 | 0.019 | 0.038 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.041 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".