Psychological and pharmacological treatments for generalized anxiety disorder (GAD): a meta-analysis of randomized controlled trials
Bibliographic record
Abstract
The purpose of this meta-analysis was to provide updated pooled effect sizes of evidence-based psychotherapies and medications for generalized anxiety disorder (GAD) and to investigate potential moderators of outcomes. Seventy-nine randomized controlled trials (RCT) including 11,002 participants with a diagnosis of GAD were included in a meta-analysis that tested the efficacy of psychotherapies or medications for GAD. Psychotherapy showed a medium to large effect size (g = 0.76) and medication showed a small effect size (g = 0.38) on GAD outcomes. Psychotherapy also showed a medium effect on depression outcomes (g = 0.64) as did medications (g = 0.59). Younger age was associated with a larger effect size for psychotherapy (p < 0.05). There was evidence of publication bias in psychotherapy studies. This analysis found a medium to large effect for empirically supported psychotherapy interventions on GAD outcomes and a small effect for medications on GAD outcomes. Both groups showed a medium effect on depression outcomes. Because medication studies had more placebo control conditions than inactive conditions compared to psychotherapy studies, effect sizes between the domains should not be compared directly. Patient age should be further investigated as a potential moderator in psychotherapy outcomes in GAD.
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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 | 0.016 | 0.038 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.036 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".