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Record W2067622680 · doi:10.1109/iembs.2011.6090832

Effect of functional electrical stimulation (FES) combined with robotically assisted treadmill training on the EMG profile

2011· article· en· W2067622680 on OpenAlexaff
Sina Askari, T. Chao, Lindsay Conn, Elizabeth Partida, T. Lazzaretto, Pamela Anne See, Cheryl H. T. Chow, Ray D. de Leon, Deborah Won

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMuscle activation and electromyography studies
Canadian institutionsMcMaster University
FundersNational Institute of Neurological Disorders and Stroke
KeywordsFunctional electrical stimulationRehabilitationPhysical medicine and rehabilitationSpinal cord injuryNeurophysiologyTreadmillSpinal cordStimulationMedicineElectromyographyGaitPhysical therapy

Abstract

fetched live from OpenAlex

Functional electrical stimulation (FES) is used to assist spinal cord injury patients during walking. However, FES has yet to be shown to have lasting effects on the underlying neurophysiology which lead to long-term rehabilitation. A new approach to FES has been developed by which stimulation is timed to robotically controlled movements in an attempt to promote long-term rehabilitation of walking. This approach was tested in a rodent model of spinal cord injury. Rats who received this FES therapy during a 2-week training period exhibited peak EMG activity during the appropriate phase of the gait cycle; whereas, rats who received stimulation which was randomly timed with respect to their motor activity exhibited no clear pattern in their EMG profile. These results from our newly developed FES system serve as a launching point for many future studies to test and understand the long-term effect of FES on spinal cord rehabilitation.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.031
GPT teacher head0.216
Teacher spread0.185 · 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 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

Citations6
Published2011
Admission routes1
Has abstractyes

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