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Record W2328941139 · doi:10.3109/09593985.2015.1110848

A single session of open kinetic chain movements emphasizing speed improves speed of movement and modifies postural control in stroke

2016· article· en· W2328941139 on OpenAlexafffund
Vicki L. Gray, Tanya D. Ivanova, S. Jayne Garland

Bibliographic record

VenuePhysiotherapy Theory and Practice · 2016
Typearticle
Languageen
FieldEngineering
TopicMuscle activation and electromyography studies
Canadian institutionsUniversity of British Columbia
FundersHeart and Stroke Foundation of Canada
KeywordsBicepsPhysical medicine and rehabilitationElectromyographyMedicinePhysical therapyBalance (ability)Task (project management)

Abstract

fetched live from OpenAlex

Little attention has been given to training speed of movement, even though functional activities require quick submaximal contractions. Closed kinetic chain (CKC) exercises are considered more functional; however, the best method for training speed is not known. A single bout of open kinetic chain (OKC) exercises emphasizing speed was performed to determine whether movement velocity and muscle activation would improve in a single session and whether the improvements transfer to a physiological balance task. Eleven participants <1 year post-stroke performed an arm raise task before and after a single session of fast OKC exercises. Surface electromyography (EMG) from soleus (SOL), tibialis anterior (TA), biceps femoris (BF) and rectus femoris (RF) muscles, peak velocity and average power were recorded during the OKC exercises. EMG from SOL, TA, BF and RF and center of pressure (COP) velocity were measured during arm raise task. At the end of the OKC exercises, velocity, power and TA, BF and RF EMG area increased. The arm acceleration and BF EMG area increased significantly during the arm raise. The improvements observed at the end of the OKC exercises transferred to the arm raise task. The improvements in balance were comparable to those previously seen after CKC exercises.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.143
Threshold uncertainty score0.370

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 communication0.0000.001
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 head0.015
GPT teacher head0.287
Teacher spread0.273 · 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 designBench or experimental
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

Citations8
Published2016
Admission routes2
Has abstractyes

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