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Record W2554529016 · doi:10.4137/jen.s40827

Neurocognitive Treatment for a Patient with Alzheimer's Disease Using a Virtual Reality Navigational Environment

2016· article· en· W2554529016 on OpenAlex
Paul White, Zahra Moussavi

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Experimental Neuroscience · 2016
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsNeurocognitiveVirtual realityLandmarkCognitionDiseasePsychologyCognitive trainingMedicineHuman–computer interactionCognitive psychologyComputer scienceArtificial intelligencePsychiatry

Abstract

fetched live from OpenAlex

In this case study, a man at the onset of Alzheimer's disease (AD) was enrolled in a cognitive treatment program based upon spatial navigation in a virtual reality (VR) environment. We trained him to navigate to targets in a symmetric, landmark-less virtual building. Our research goals were to determine whether an individual with AD could learn to navigate in a simple VR navigation (VRN) environment and whether that training could also bring real-life cognitive benefits. The results show that our participant learned to perfectly navigate to desired targets in the VRN environment over the course of the training program. Furthermore, subjective feedback from his primary caregiver (his wife) indicated that his skill at navigating while driving improved noticeably and that he enjoyed cognitive improvement in his daily life at home. These results suggest that VRN treatments might benefit other people with AD.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.094
Threshold uncertainty score0.346

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.065
GPT teacher head0.362
Teacher spread0.296 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it