P.086 Randomized controlled trial evaluating virtual reality exposure therapy on epilepsy/seizure-specific interictal anxiety in people with epilepsy
Bibliographic record
Abstract
Background: Over 28% of people with epilepsy (PwE) experience anxiety related to their seizures, yet research on interventions for epilepsy-specific (ES) interictal anxiety remains limited. The community-based pilot, ’AnxEpi-VR,’ tested virtual reality exposure therapy (VR-ET), showing potential effectiveness and laying the groundwork for evaluating its impact on ES-anxiety in the present randomized controlled trial. Methods: Fourteen PwE admitted to the Epilepsy Monitoring Unit at Toronto Western Hospital used a 360-degree VR-intervention twice daily for five minutes up to ten days. The control group (n=7) viewed neutral VR environments (e.g., beach, forest scenes), while the experimental group (n=7) experienced VR-ET targeting ES anxiety (e.g., subway seizure scenarios). Data was collected at baseline, pre and post-VR exposure, post-intervention, and one-month follow-up using self-report questionnaires, semi-structured interviews, and VR-usage metrics. Clinicaltrials.gov NCT06028945. Results: At baseline, 71% of the control group and 83% of the exposure group had a brEASI score ≥7, indicative of an anxiety disorder. Post-intervention, this decreased to 57% and 50%, respectively. The average Fast Motion Sickness rating was 1.29, indicating minimal motion sickness. Of the exposure participants, 71% felt the scenarios simulated their real-world anxiety triggers. Conclusions: VR-ET was well-tolerated in PwE. Future recommendations include diversifying scenarios, adding interactive features, and improving software connectivity.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.022 | 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".