Experiential Cognitive Therapy in the Treatment of Panic Disorders with Agoraphobia: A Controlled Study
Bibliographic record
Abstract
The use of a multicomponent cognitive-behavioral treatment strategy for panic disorder with agoraphobia is actually one of the preferred therapeutic approaches for this disturbance. This method involves a mixture of cognitive and behavioral techniques that are intended to help patients identify and modify their dysfunctional anxiety-related thoughts, beliefs and behavior. The paper presents a new treatment protocol for Panic Disorder and Agoraphobia, named Experiential-Cognitive Therapy (ECT) that integrates the use of virtual reality (VR) in a multicomponent cognitive-behavioral treatment strategy. The VR software used for the trial is freely downloadable: www.cyberpsychology.info/try.htm. Moreover, the paper presents the result of a controlled study involving 12 consecutive patients aged 35-53. The selected subjects were randomly divided in three groups: ECT group, that experienced the Cognitive Behavioral Therapy-Virtual Reality assisted treatment (eight sessions), a CBT group that experienced the traditional Cognitive Behavioral approach (12 sessions) and a waiting list control group. The data showed that both CBT and ECT could significantly reduce the number of panic attacks, the level of depression and both state and trait anxiety. However, ECT procured these results using 33% fewer sessions than CBT. This datum suggests that ECT could be better than CBT in relation to the "cost of administration," justifying the added use of VR equipment in the treatment of panic disorders.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".