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Record W3032947492 · doi:10.1080/17518423.2020.1773956

A Case of Neuromuscular Electrical Stimulation for Childhood Stroke Hyperkinesis: A Brief Report

2020· article· en· W3032947492 on OpenAlexaff
Megan J. Metzler, Lauran Cole, Adam Kirton

Bibliographic record

VenueDevelopmental Neurorehabilitation · 2020
Typearticle
Languageen
FieldMedicine
TopicCerebral Palsy and Movement Disorders
Canadian institutionsUniversity of CalgaryAlberta Children's Hospital
Fundersnot available
KeywordsHyperkinesisPsychologyPhysical medicine and rehabilitationMovement assessmentProprioceptionTest (biology)Stroke (engine)SensationPopulationPhysical therapyDevelopmental psychologyMedicineMotor skillNeuroscience

Abstract

fetched live from OpenAlex

AIM: Some conditions within specific populations are so rare rigorous evidence is unavailable. Childhood hyperkinesis is one example, yet presents an opportunity to examine sensation's contribution to motor function. METHODS: The patient experienced functional difficulty from hyperkinesis as a result of childhood stroke. Home-based passive neuromuscular electrical stimulation (NMES) was implemented an hour/day, six days/week, over 6 weeks (36 hours). Clinical and robotic measures (Assisting Hand Assessment, Box and Block Test, Jebsen Taylor Test of Hand Function, Kinarm) were administered before and after the intervention and at 9 months. RESULTS: NMES was feasible and well tolerated. Clinically important gains of arm function were maintained at 9 months. Robotic measures showed improved hyperkinesis, namely reduced movement segmentation and improved target approximation, in addition to improved proprioceptive function after NMES. CONCLUSION: This case study illustrates the use of NMES within a previously unexplored population and highlights the potential importance of sensory systems to motor gains.

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)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0040.002
Science and technology studies0.0030.002
Scholarly communication0.0010.002
Open science0.0020.002
Research integrity0.0070.004
Insufficient payload (model declined to judge)0.0030.001

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 head0.021
GPT teacher head0.264
Teacher spread0.243 · 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 designCase report
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

Citations5
Published2020
Admission routes1
Has abstractyes

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