Assessing Cognition in Kidney Failure Using Virtual Reality Technology: A Clinical Research Protocol
Bibliographic record
Abstract
Background: Cognitive impairment is common in patients receiving dialysis and is associated with morbidity and mortality. Existing approaches to administering face-to-face cognitive screening assessments like the Montreal Cognitive Assessment (MoCA) may be challenging to undertake in dialysis. Virtual reality (VR) technology may be a novel way to assess cognitive function in patients on dialysis. Objective: In a cohort of patients undergoing hemodialysis, the primary objective of this study is to evaluate the test-retest reliability, diagnostic performance, and agreement of an MoCA, generated using VR-based cognitive testing, to a face-to-face MoCA. Secondary objectives are to (1) evaluate changes in cognitive function over time using the VR-generated MoCA, (2) examine associations between cognitive impairment and mortality or hospitalization, and (3) assess the usability of VR-based cognitive testing. Design: This is a prospective cohort study (conducted from 2025-2028). Setting: Hemodialysis units affiliated with the Nova Scotia Health Renal Program. Patients: Incident (within 3 months of dialysis initiation) and prevalent patients receiving hemodialysis. Measurements: Cognitive function will be assessed using the React Neuro VR Headset and the paper-based MoCA. The VR cognitive assessment will include tests such as Smooth Pursuit, Trail Making A/B, Letter/Category Fluency, Boston Naming, Stroop, and Digit Span (Forward/Backward). The results of these tests will be used to generate an MoCA score using device software. Methods: The VR cognitive tests and face-to-face MoCA assessments will be conducted at baseline and week 2, with the order of assessments randomly determined. Subsequent VR cognitive assessments will be conducted once every 3 months (up to 12 months). Agreement will be assessed using Cohen's kappa (dichotomizing the MoCA at <24), and existing approaches for continuous MoCA scores. Test-retest reliability will be assessed using a similar approach comparing baseline and 2-week scores. Associations between the VR-generated MoCA and outcomes will be analyzed using appropriate regression methods. Results: To date, we have recruited 84 patients, 75 of whom have completed at least their baseline assessment. Limitations: Potential challenges in VR implementation and patient adaptation, as well as the loud and distracting dialysis environment, could impact performance in cognitive assessments. Conclusions: This proposed study aims to evaluate test-retest reliability, performance, and agreement between a VR-generated and face-to-face MoCA. The VR technology may provide a reliable alternative to traditional cognitive testing in dialysis patients. The findings can be used to assist in the early identification of patients with cognitive impairment and may also pave the way for future research, including VR-delivered interventions to improve cognitive function and health outcomes in this population.
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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 | 0.026 | 0.015 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.025 | 0.007 |
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".