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Record W2064228716 · doi:10.1097/pep.0b013e318227ca0f

An Intensive Virtual Reality Program Improves Functional Balance and Mobility of Adolescents With Cerebral Palsy

2011· article· en· W2064228716 on OpenAlexaff
Marie Brien, Heidi Sveistrup

Bibliographic record

VenuePediatric Physical Therapy · 2011
Typearticle
Languageen
FieldMedicine
TopicCerebral Palsy and Movement Disorders
Canadian institutionsChildren's Hospital of Eastern Ontario
Fundersnot available
KeywordsCerebral palsyBalance (ability)Gross Motor Function Classification SystemPhysical medicine and rehabilitationPhysical therapyMedicineIntervention (counseling)Gross motor skillMotor skillPsychology

Abstract

fetched live from OpenAlex

In Brief Purpose: To examine functional balance and mobility in adolescents with cerebral palsy classified at Gross Motor Function Classification System (GMFCS) level I following an intensive short-duration virtual reality (VR) intervention. Methods: Single-subject, multiple-baseline design with 4 adolescents. Outcomes included the Community Balance and Mobility Scale (CB&M), the 6-Minute Walk Test (6MWT), the Timed Up and Down Stairs, and the Gross Motor Function Measure Dimension E. Assessments were recorded 3 to 6 times at baseline, 5 times during intervention, and 4 times at follow-up. Daily 90-minute VR intervention was completed for 5 consecutive days. Visual, statistical, and clinical significance analyses were used. Results: Statistically significant improvements were shown in all adolescents on CB&M and 6MWT. True change was recorded in all for the CB&M and in 3 for the 6MWT. Conclusions: Functional balance and mobility in adolescents with cerebral palsy classified at GMFCS level I improve with intense, short duration VR intervention, and changes are maintained at 1-month posttraining. These investigators report that functional balance and mobility in adolescents with cerebral palsy classified at GMFCS level I improved with intense, short duration VR intervention, and the improvements continued to be present at 1-month post training.

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 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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.056
Threshold uncertainty score0.488

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 head0.029
GPT teacher head0.283
Teacher spread0.254 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations129
Published2011
Admission routes1
Has abstractyes

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