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Record W2244932815 · doi:10.1123/mcj.16.2.265

The Effect of Muscle Fatigue on Position Sense in an Upper Limb Multi-joint Task

2012· article· en· W2244932815 on OpenAlexaff
Amirhossein K Vafadar, Julie N. Côté, Philippe S. Archambault

Bibliographic record

VenueMotor Control · 2012
Typearticle
Languageen
FieldNeuroscience
TopicMotor Control and Adaptation
Canadian institutionsMcGill University
Fundersnot available
KeywordsPhysical medicine and rehabilitationOrientation (vector space)Muscle fatigueTask (project management)Position (finance)Upper limbPhysical therapyPsychologyMedicineMathematicsElectromyographyEngineeringGeometry

Abstract

fetched live from OpenAlex

The purpose of this study was to estimate the extent to which muscle fatigue can impact on the position sense in the upper limb. Twelve healthy volunteers were asked to do a reaching task while grasping a wooden block and match the block's position with a corresponding target displayed on a flat screen, without vision. Following that, subjects performed resistive exercises with Thera-band strips until fatigue was induced and then the position sense task was repeated. A significant change in the endpoint position was observed after fatigue, in the up/down direction (p ≤ .001). The variability of endpoint positions in up/down direction was also significantly increased after fatigue (p ≤.03). There was no significant change in endpoint orientation but there was a significant fatigue × orientation effect on endpoint rotational variability. In a follow-up experiment, a group of subjects repeated the same protocol, but with a period of quiet rest between the two position sense tasks. In that group, there were no differences in endpoint position, orientation or variability. Muscle fatigue is an important factor that should be taken into consideration during the treatment of musculoskeletal injuries as well as athletic training.

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)0.003
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
Metaresearch0.0000.003
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 head0.028
GPT teacher head0.268
Teacher spread0.240 · 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

Citations28
Published2012
Admission routes1
Has abstractyes

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