Cognitive behavioral therapy with adaptive virtual reality exposure vs. cognitive behavioral therapy with in vivo exposure in the treatment of social anxiety disorder: A study protocol for a randomized controlled trial
Bibliographic record
Abstract
Background: Social anxiety disorder (SAD) has a high prevalence and an early onset with recovery taking decades to occur. Current evidence supports the efficacy of cognitive behavioral therapy (CBT) with virtual reality (VR) exposure. However, the evidence is based on a sparse number of studies with predominantly small sample sizes. There is a need for more trials investigating the optimal way of applying VR based exposure for SAD. In this trial, we will test the efficacy of CBT with adaptive VR exposure allowing adjustment of the exposure based on real-time monitoring of the participants's anxiety level. Methods: (CBT-Exp), (2) CBT including exposure therapy using individually tailored VR-content and a system to track anxiety levels (CBT-ExpVR). Treatment is individual, manual-based and consists of 10 weekly sessions with a duration of 60 min. The study includes 90 participants diagnosed with SAD. Assessments are carried out pre-treatment, mid-treatment and at follow-up (6 and 12 months). The primary outcome is the mean score on the Social Interaction Anxiety Scale (SIAS) with the primary endpoint being post-treatment. Discussion: The study adds to the existing knowledge by assessing the efficacy of CBT with adaptive VR exposure. The study has high methodological rigor using a randomized controlled trial with a large sample size that includes follow-up data and validated measures for social anxiety outcomes. Clinical trial registration: ClinicalTrials.gov, identifier: NCT05302518.
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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 | 0.027 | 0.023 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.006 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.052 | 0.008 |
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".