Ecological Virtual Reality Evaluation of Neglect Symptoms (EVENS): Effects of Virtual Scene Complexity in the Assessment of Poststroke Unilateral Spatial Neglect
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: Unilateral spatial neglect (USN) is a highly prevalent and disabling poststroke impairment. USN is traditionally assessed with paper-and-pencil tests that lack ecological validity, generalization to real-life situations and are easily compensated for in chronic stages. Virtual reality (VR) can, however, counteract these limitations. OBJECTIVE: We aimed to examine the feasibility of a novel assessment of USN symptoms in a functional shopping activity, the Ecological VR-based Evaluation of Neglect Symptoms (EVENS). METHODS: EVENS is immersive and consists of simple and complex 3-dimensional scenes depicting grocery shopping shelves, where joystick-based object detection and navigation tasks are performed while seated. Effects of virtual scene complexity on navigational and detection abilities in patients with (USN+, n = 12) and without (USN-, n = 15) USN following a right hemisphere stroke and in age-matched healthy controls (HC, n = 9) were determined. RESULTS: Longer detection times, larger mediolateral deviations from ideal paths and longer navigation times were found in USN+ versus USN- and HC groups, particularly in the complex scene. EVENS detected lateralized and nonlateralized USN-related deficits, performance alterations that were dependent or independent of USN severity, and performance alterations in 3 USN- subjects versus HC. CONCLUSION: EVENS' environmental changing complexity, along with the functional tasks of far space detection and navigation can potentially be clinically relevant and warrant further empirical investigation. Findings are discussed in terms of attentional models, lateralized versus nonlateralized deficits in USN, and tasks-specific mechanisms.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it