Pre-Validation of a Virtual Reality Tool to Quantify the Severity of Friedreich's Ataxia
Bibliographic record
Abstract
Existing clinical scales to evaluate the severity of Friedreich's ataxia (FA) impairment rapidly reach a plateau when patients lose the ability to walk, and they rely on the examiner to quantify ataxia. Therefore, more objective tools are necessary to complement the neurological examination and distinguish subtle changes in ataxia over time. Using virtual reality (VR), we aim to develop precise tools to measure the progression of FA. The purpose of this study was to evaluate a series of 5 different upper limb tasks in VR with FA patients in order to determine how well they were tolerated, if they allow to distinguish FA patients from a control group, and if they correlate to clinical measures. Twelve FA patients and 9 healthy partici-pants underwent traditional assessment of upper limb function and of FA severity (patients only), performed the series of five VR task, and completed a subjective evaluation. Only 6 out of 12 FA patients were able to successfully complete all tasks, primarily due to the challenges associated with manipulating the controllers for patients with advanced stages of FA. Different performance metrics of the tasks were significantly different between groups and had strong correlations to a clinical assessment scale. All participants reported very low simulator sickness and a high level of virtual presence. The results of this study demonstrate the feasibility of using VR with FA patients, although adaptation of the technology may be necessary for those with more severe impairments.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".