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Record W2146737178 · doi:10.1186/1743-0003-4-20

Virtual reality environments for post-stroke arm rehabilitation

2007· article· en· W2146737178 on OpenAlex

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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of NeuroEngineering and Rehabilitation · 2007
Typearticle
Languageen
FieldMedicine
TopicStroke Rehabilitation and Recovery
Canadian institutionsUniversité LavalUniversité de MontréalMcGill UniversityJewish Rehabilitation Hospital
FundersCanadian Institutes of Health Research
KeywordsVirtual realityPhysical medicine and rehabilitationHuman–computer interactionComputer scienceRehabilitationSalientElevatorMotor learningVirtual machinePsychologyMedicineArtificial intelligenceNeuroscienceEngineering

Abstract

fetched live from OpenAlex

INTRODUCTION: Optimal practice and feedback elements are essential requirements for maximal motor recovery in patients with motor deficits due to central nervous system lesions. METHODS: A virtual environment (VE) was created that incorporates practice and feedback elements necessary for maximal motor recovery. It permits varied and challenging practice in a motivating environment that provides salient feedback. RESULTS: The VE gives the user knowledge of results feedback about motor behavior and knowledge of performance feedback about the quality of pointing movements made in a virtual elevator. Movement distances are related to length of body segments. CONCLUSION: We describe an immersive and interactive experimental protocol developed in a virtual reality environment using the CAREN system. The VE can be used as a training environment for the upper limb in patients with motor impairments.

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)0.001
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.872
Threshold uncertainty score0.491

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
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 head0.008
GPT teacher head0.258
Teacher spread0.250 · 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