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Record W2769335421 · doi:10.1109/mpul.2017.2750858

Virtual Rehabilitation with Children: Challenges for Clinical Adoption [From the Field]

2017· article· en· W2769335421 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.

Bibliographic record

VenueIEEE Pulse · 2017
Typearticle
Languageen
FieldMedicine
TopicStroke Rehabilitation and Recovery
Canadian institutionsSunny Hill Health Centre for ChildrenUniversity of British Columbia
Fundersnot available
KeywordsRehabilitationVirtual realityAugmented realityRoboticsTelerehabilitationPhysical medicine and rehabilitationMotion (physics)Field (mathematics)Human–computer interactionMixed realityPsychologyComputer scienceMedicineRobotArtificial intelligenceTelemedicineHealth care

Abstract

fetched live from OpenAlex

Virtual, augmented, and mixed reality environments are increasingly being developed and used to address functional rehabilitation goals related to physical, cognitive, social, and psychological impairments. For example, a child with an acquired brain injury may participate in virtual rehabilitation to address impairments in balance, attention, turn taking, and engagement in therapy. The trend toward virtual rehabilitation first gained momentum with the adoption of commercial off-the-shelf active video gaming consoles (e.g., Nintendo Wii and XBox). Now, we are seeing the rapid emergence of customized rehabilitation-specific systems that integrate technological advances in virtual reality, visual effects, motion tracking, physiological monitoring, and robotics.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.427
Threshold uncertainty score0.245

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
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 head0.046
GPT teacher head0.367
Teacher spread0.321 · 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