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Record W2018843984 · doi:10.1089/cpb.2006.9.123

Feasibility, Motivation, and Selective Motor Control: Virtual Reality Compared to Conventional Home Exercise in Children with Cerebral Palsy

2006· article· en· W2018843984 on OpenAlexaff
C. Bryanton, Jean Paul Bosse, Marie Brien, John McLean, Anna McCormick, Heidi Sveistrup

Bibliographic record

VenueCyberPsychology & Behavior · 2006
Typearticle
Languageen
FieldMedicine
TopicCerebral Palsy and Movement Disorders
Canadian institutionsOntario HIV Treatment NetworkUniversity of Ottawa
FundersInternational Research and Exchanges Board
KeywordsCerebral palsyPhysical medicine and rehabilitationVirtual realityAnklePhysical therapyPsychologySpastic cerebral palsySpasticRange of motionMotor controlMedicineComputer scienceHuman–computer interactionNeuroscience

Abstract

fetched live from OpenAlex

Children with cerebral palsy (CP) have difficulty controlling and coordinating voluntary muscle, which results in poor selective control of muscle activity. Children with spastic CP completed ankle selective motor control exercises using a virtual reality (VR) exercise system and conventional (Conv) exercises. Ankle movements were recorded with an electrogoniometer. Children and their parents were asked to comment on their interest in the exercise programs. Greater fun and enjoyment were expressed during the VR exercises. Children completed more repetitions of the Conv exercises, but the range of motion and hold time in the stretched position were greater during VR exercises. These data suggest that using VR to elicit or guide exercise may improve exercise compliance and enhance exercise effectiveness.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
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)0.0010.000

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 head0.019
GPT teacher head0.293
Teacher spread0.274 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNon-randomized trial
Domainnot available
GenreEmpirical

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".

Quick stats

Citations346
Published2006
Admission routes1
Has abstractyes

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