Virtual Reality Assisted Cognitive Behavioral Therapy for the Treatment of Panic Disorders with Agoraphobia
Bibliographic record
Abstract
The chapter describes the characteristics of the Experiential-Cognitive Therapy (ECT) protocol for Panic Disorder and Agoraphobia. The goal of ECT is to decondition fear reactions, to modify misinterpretational cognition related to panic symptoms and to reduce anxiety symptoms. This is possible in an average of eight sessions of treatment plus an assessment phase and booster sessions, through the integration of Virtual Experience and traditional cognitive-behavioral techniques. We decided to employ the techniques included in the cognitive-behavioral approach because they showed high levels of efficacy. Through virtual environments we can gradually expose the patient to feared situation: virtual reality consent to re-create in our clinical office a real experiential world. The patient faces the feared stimuli in a context that is nearer to reality than imagination. For ECT we developed the Virtual Environments for Panic Disorders--VEPD--virtual reality system. VEPD is a 4-zone virtual environment developed using the Superscape VRT 5.6 toolkit. The four zones reproduce different potentially fearful situations--an elevator, a supermarket, a subway ride, and large square. In each zone the characteristics of the anxiety-related experience are defined by the therapist through a setup menu.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".