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Record W1965306166 · doi:10.2466/26.10.pms.117x12z2

Perception of Weight-Bearing and Effort Distribution during Sit-to-Stand in Individuals Post-Stroke

2013· article· en· W1965306166 on OpenAlexaff
Anabèle Brière, Sylvie Nadeau, Séléna Lauzière, Denis Gravel

Bibliographic record

VenuePerceptual and Motor Skills · 2013
Typearticle
Languageen
FieldEngineering
TopicMuscle activation and electromyography studies
Canadian institutionsInstitut de Readaptation Gingras Lindsay de MontrealUniversité de MontréalCentre for Interdisciplinary Research in Rehabilitation
Fundersnot available
KeywordsPerceptionWeight-bearingPhysical medicine and rehabilitationPsychologyPhysical therapyWeight distributionDistribution (mathematics)AudiologyMedicineMathematicsSurgeryEngineering

Abstract

fetched live from OpenAlex

The objectives were to assess whether individuals who are poststroke can rate their perceived knee effort distribution during sit-to-stand tasks in various foot positions, to quantify their errors of perception and to compare these to their errors in weight-bearing perception. Weight-bearing distribution was assessed in hemiparetic participants (N = 19) using a force platform. Electromyographic (EMG) data normalized to maximal EMG values were used to quantify knee effort distribution. The difference between participants' real weight bearing and knee effort and the perceived values rated on a visual analog scale defined their errors of weight-bearing and effort perception. The perception of effort and weight bearing, and the errors therein, were compared among the four foot positions. Participants perceived only the changes induced by the different foot positions on their weightbearing distribution, not on their knee effort distribution, and they made greater perception errors with the knee effort distribution than with the weight-bearing distribution.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.717
Threshold uncertainty score0.500

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 head0.004
GPT teacher head0.193
Teacher spread0.189 · 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

Citations10
Published2013
Admission routes1
Has abstractyes

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