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Record W2068620558 · doi:10.3109/17518420903357753

Virtual reality as a therapeutic modality for children with cerebral palsy

2010· review· en· W2068620558 on OpenAlexafffund
Laurie Snider, Annette Majnemer, Vasiliki Darsaklis

Bibliographic record

VenueDevelopmental Neurorehabilitation · 2010
Typereview
Languageen
FieldMedicine
TopicCerebral Palsy and Movement Disorders
Canadian institutionsMcGill University
FundersMcGill University Health Centre
KeywordsCerebral palsyVirtual realityPsychologyPhysical medicine and rehabilitationModality (human–computer interaction)Quality of life (healthcare)Randomized controlled trialIntervention (counseling)Physical therapyDevelopmental psychologyMedicineComputer sciencePsychotherapistPsychiatryHuman–computer interactionSurgery

Abstract

fetched live from OpenAlex

OBJECTIVE: The evidence for using virtual reality (VR) with children with cerebral palsy (CP) was examined. METHODS: A search of 13 electronic databases identified all types of studies examining VR as an intervention for children with CP. The most recent article included was published in October 2008. For each study, the quality of the methods was assessed using the appropriate scale. A total of 19 articles were retrieved. Thirteen studies from 11 articles were included in the final analysis. RESULTS: Outcomes documented brain reorganization/plasticity, motor capacity, visual-perceptual skills, social participation and personal factors. Two studies were randomized controlled trials. These reported conflicting results regarding motor outcomes. Twelve of the 13 studies presented positive outcomes in at least one domain. CONCLUSIONS: VR has potential benefits for children with CP. However, the current level of evidence is poor and empirical data is lacking. Future methodologically rigorous studies are required.

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)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0040.004
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.037
GPT teacher head0.327
Teacher spread0.291 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations213
Published2010
Admission routes2
Has abstractyes

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Same venueDevelopmental NeurorehabilitationSame topicCerebral Palsy and Movement DisordersFrench-language works237,207