VReeze: an open-source virtual reality for the examination of freezing of gait in Parkinson’s disease – a study design of a crossover repeated measures study for validation
Bibliographic record
Abstract
INTRODUCTION: Parkinson's disease is the second most prevalent neurodegenerative disease worldwide, with up to 70% of patients exhibiting freezing of gait (FOG). FOG is defined as transient episodes when one is unable to effectively engage in the stepping process (despite the intention to walk), which decreases or completely ceases forward movement. Although several FOG triggers have been identified, eliciting FOG remains challenging in research, hindering progress in research and therapy. Virtual reality (VR) offers a promising approach to evoke FOG during overground walking by combining environmental and neuropsychological triggers. This project aims to validate an existing open-source VR-FOG toolbox that integrates multiple triggers. METHODS: A within-subject repeated measures crossover study design with a 1-hour washout period will be used for this project to validate the VR-FOG toolbox. This will consist of three blocks (baseline non-VR, VR non-FOG and VR-FOG). All participants will first complete a baseline walking trial without VR, then be randomised to either the VR non-FOG environment-a virtual replica of the laboratory-or the VR-FOG environment containing multiple virtual FOG triggers. After a 1-hour washout period, they will complete the remaining VR condition. A crossover design will minimise ordering effects of VR conditions on FOG frequency and duration. Twenty participants with Parkinson's disease with FOG will be tested at St. Pölten University of Applied Sciences (Austria) and 20 at the University of Exeter (UK) and will be recruited from local communities. Multisite testing will verify that the VR-FOG environment triggers FOG regardless of testing location. ETHICS AND DISSEMINATION: Ethical approval was obtained from the Lower Austrian Ethics Commission and the University of Exeter review boards. All data will be anonymised, used solely for this project and securely stored in General Data Protection Regulation-compliant repositories. Study results will be presented at scientific conferences and published in peer-reviewed journals.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".