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Record W2083707350 · doi:10.5402/2012/295038

Weight-Bearing and Effort Distributions at the Lower Limbs during the Five-Repetition Sit-to-Stand Test in Hemiparetic and Healthy Individuals

2011· article· en· W2083707350 on OpenAlexafffund
Anabèle Brière, Sylvie Nadeau, Séléna Lauzière, Denis Gravel

Bibliographic record

VenueISRN Rehabilitation · 2011
Typearticle
Languageen
FieldEngineering
TopicMuscle activation and electromyography studies
Canadian institutionsInstitut de Readaptation Gingras Lindsay de MontrealUniversité de MontréalCentre for Interdisciplinary Research in Rehabilitation
FundersRéseau Provincial de Recherche en Adaptation-Réadaptation
KeywordsPhysical medicine and rehabilitationRepetition (rhetorical device)Test (biology)PsychologyPhysical therapyTask (project management)MathematicsMedicineEngineering

Abstract

fetched live from OpenAlex

Background. The weight-bearing (WB) and effort distributions during the five-repetition sit-to-stand test (5R-STS) were assessed in healthy and hemiparetic subjects and were compared to the distributions obtained for a single STS task (1-STS).Methods. Eighteen hemiparetic subjects and 12 controls were included. The WB distribution and time were computed using the vertical ground reaction forces. The knee muscles' effort distribution was quantified with the electromyographic (EMG) data of the STS transfers expressed relatively to the EMG values of maximal strength assessments.Results. In both groups, the time, WB, and effort distributions did not differ between repetitions of the 5R-STS test. The WB and effort distributions of the first repetition were more asymmetrical than those for the 1-STS for the hemiparetic subjects only.Conclusions. Since no changes were found between repetitions, the 5R-STS test might not be demanding enough. The hemiparetic subjects adopt different WB and effort distribution strategies according to the number of STSs to complete.

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)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.007
GPT teacher head0.211
Teacher spread0.204 · 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

Citations2
Published2011
Admission routes2
Has abstractyes

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Same venueISRN RehabilitationSame topicMuscle activation and electromyography studiesFrench-language works237,207