Group transdiagnostic cognitive-behavior therapy for anxiety disorders: a pragmatic randomized clinical trial
Bibliographic record
Abstract
Abstract Background Transdiagnostic group cognitive-behavioral therapy (tCBT) is a delivery model that could help overcome barriers to large-scale implementation of evidence-based psychotherapy for anxiety disorders. The aim of this study was to assess the effectiveness of combining group tCBT with treatment-as-usual (TAU), compared to TAU, for the treatment of anxiety disorders in community-based mental health care. Methods In a multicenter single-blind, two-arm pragmatic superiority randomized trial, we recruited participants aged 18–65 who met DSM-5 criteria for principal diagnoses of generalized anxiety disorder, social anxiety disorder, panic disorder, or agoraphobia. Group tCBT consisted of 12 weekly 2 h sessions. There were no restrictions for TAU. The primary outcome measures were the Beck Anxiety Inventory (BAI) and clinician severity rating from the Anxiety and Related Disorders Interview Schedule for DSM-5 (ADIS-5) for the principal anxiety disorder at post-treatment, with intention-to-treat analysis. Results A total of 231 participants were randomized to either tCBT + TAU (117) or TAU (114), with outcome data available for, respectively, 95 and 106. Results of the mixed-effects regression models showed superior improvement at post-treatment for participants in tCBT + TAU, compared to TAU, for BAI [p < 0.001; unadjusted post-treatment mean (s.d.): 13.20 (9.13) v. 20.85 (10.96), Cohen's d = 0.76] and ADIS-5 [p < 0.001; 3.27 (2.19) v. 4.93 (2.00), Cohen's d = 0.79]. Conclusions Our findings suggest that the addition of group tCBT into usual care can reduce symptom severity in patients with anxiety disorders, and support tCBT dissemination in routine community-based care.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".