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Record W2009790991 · doi:10.1177/0883073813484088

Functional Electrical Stimulation Therapy for Recovery of Reaching and Grasping in Severe Chronic Pediatric Stroke Patients

2013· article· en· W2009790991 on OpenAlexafffund
Naaz Kapadia, Mary K. Nagai, Vera Zivanovic, Janet Bernstein, Janet Woodhouse, Peter Rumney, Miloš R. Popović

Bibliographic record

VenueJournal of Child Neurology · 2013
Typearticle
Languageen
FieldMedicine
TopicStroke Rehabilitation and Recovery
Canadian institutionsHolland Bloorview Kids Rehabilitation HospitalUniversity of TorontoToronto Rehabilitation Institute
FundersToronto Rehabilitation Institute
KeywordsRehabilitationPhysical medicine and rehabilitationChronic strokePhysical therapyStroke (engine)Functional electrical stimulationMedicineOccupational therapyRetrainingGRASPTest (biology)Stimulation

Abstract

fetched live from OpenAlex

Stroke affects 2.7 children per 100,000 annually, leaving many of them with lifelong residual impairments despite intensive rehabilitation. In the present study the authors evaluated the effectiveness of 48 hours of transcutaneous functional electrical stimulation therapy for retraining voluntary reaching and grasping in 4 severe chronic pediatric stroke participants. Participants were assessed using the Rehabilitation Engineering Laboratory Hand Function Test, Quality of Upper Extremity Skills Test, Pediatric Evaluation of Disability Inventory, and Assisting Hand Assessment. All participants improved on all measures. The average change scores on selected Rehabilitation Engineering Laboratory Hand Function Test components were 14.5 for object manipulation (P = .042), 0.78 Nm for instrumented cylinder (P = .068), and 14 for wooden blocks (P = .068) and on the grasp component of Quality of Upper Extremity Skills Test was 25.93 (P = .068). These results provide preliminary evidence that functional electrical stimulation therapy has the potential to improve upper limb function in severe chronic pediatric stroke patients.

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 score0.029
Threshold uncertainty score0.224

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 head0.010
GPT teacher head0.243
Teacher spread0.233 · 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

Citations49
Published2013
Admission routes2
Has abstractyes

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