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Record W2571365537 · doi:10.15453/2168-6408.1262

Outcome Measures with COPM of Children using a Wilmington Robotic Exoskeleton

2017· article· en· W2571365537 on OpenAlexaboutno aff
Tracy M. Shank, Marissa Eppes, Jobayer Hossain, Margaret Gunn, Tariq Rahman

Bibliographic record

VenueThe Open Journal of Occupational Therapy · 2017
Typearticle
Languageen
FieldMedicine
TopicStroke Rehabilitation and Recovery
Canadian institutionsnot available
Fundersnot available
KeywordsExoskeletonPowered exoskeletonPhysical medicine and rehabilitationOccupational therapyActivities of daily livingPhysical therapyElbowMedicinePsychologySurgery

Abstract

fetched live from OpenAlex

Background: The Wilmington Robotic Exoskeleton (WREX) is a body-powered, four degrees of freedom orthosis that allows gravity-minimized movement of the arm at the shoulder and elbow. We sought to measure patient satisfaction and performance with use of the WREX during activities of daily living, play, and at school. Method: Twenty-five families completed a phone interview based on the Canadian Occupational Performance Measure (COPM). These families all had a child, aged 2 to 21years, who had a neuromuscular disorder and who had used the WREX for at least eight months. The parents rated their child’s performance of and satisfaction with important activities both with and without the WREX. The scores were assessed for change between the two conditions. Results: Twenty-four out of the 25 parents reported that their child had greater levels of performance and satisfaction when they were wearing the WREX. The mean change in performance score was 3.61 points, and the mean change in satisfaction score was 4.44 points. Conclusion: Families who have a child diagnosed with a neuromuscular disorder and who uses the WREX perceived improved performance and satisfaction with the WREX during self-chosen meaningful activities.

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)0.001
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 score0.010
Threshold uncertainty score0.202

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.194
GPT teacher head0.433
Teacher spread0.239 · 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

Citations19
Published2017
Admission routes1
Has abstractyes

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