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Record W2171376131 · doi:10.1080/16501970510026007

Weight-bearing on the lower limbs in a sitting position during bilateral movement of the upper limbs in post-stroke hemiparetic subjects

2005· article· en· W2171376131 on OpenAlexaff
Sylvie Messier, Daniel Bourbonnais, Johanne Desrosiers, Yves Roy

Bibliographic record

VenueJournal of Rehabilitation Medicine · 2005
Typearticle
Languageen
FieldMedicine
TopicStroke Rehabilitation and Recovery
Canadian institutionsUniversité de MontréalCentre for Interdisciplinary Research in Rehabilitation
Fundersnot available
KeywordsHemiparesisSittingTrunkWeight-bearingPhysical medicine and rehabilitationWeaknessMedicineFoot (prosody)Balance (ability)Stroke (engine)Upper limbPhysical therapyAnatomySurgeryPhysics

Abstract

fetched live from OpenAlex

OBJECTIVE: Verify weight-bearing on the feet in a sitting position during pointing in different directions with 1 or both upper limbs. DESIGN: Comparative study. SUBJECTS: Fifteen subjects with post-stroke hemiparesis with good to very good motor recovery and 13 healthy subjects participated in the study. METHODS: The subjects were seated on a chair with each foot resting on a force plate. They had to touch with 1 or, simultaneously with both hands, 2 target(s) located in front of them or at a 45 degrees angle on either side at a standardized distance beyond their upper limb's length. The percentage of weight loading variation under each foot was measured. RESULTS: Weight-bearing on the paretic foot is reduced during unilateral and bilateral pointing in the anterior direction and 45 degrees ipsilateral to the paretic side. However, both unilateral and bilateral pointing 45 degrees contralateral to the paretic side produced symmetrical weight-bearing on both feet, paretic and non-paretic. CONCLUSION: Since the paretic muscles of the trunk are probably used to control the leaning of the trunk towards the non-paretic side, the subjects with hemiparesis may put weight on the paretic foot to compensate for trunk weakness and maintain balance.

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)0.002
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation 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 score0.412
Threshold uncertainty score0.371

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
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 integrity0.0000.001
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 head0.007
GPT teacher head0.249
Teacher spread0.242 · 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

Citations20
Published2005
Admission routes1
Has abstractyes

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