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Record W2125006625 · doi:10.1191/0269215503cr662oa

Arm and leg impairments and disabilities after stroke rehabilitation: relation to handicap

2003· article· en· W2125006625 on OpenAlexaff
Johanne Desrosiers, Francine Malouin, Daniel Bourbonnais, Carol L. Richards, Annie Rochette, Gina Bravo

Bibliographic record

VenueClinical Rehabilitation · 2003
Typearticle
Languageen
FieldMedicine
TopicStroke Rehabilitation and Recovery
Canadian institutionsUniversité LavalHealth and Social Services Centre University Institute of Geriatrics of SherbrookeUniversité de MontréalCentre for Interdisciplinary Research in RehabilitationUniversité de Sherbrooke
Fundersnot available
KeywordsRehabilitationPhysical medicine and rehabilitationStroke (engine)Activities of daily livingPsychologyPhysical therapyCohortPhysical disabilityInternational Classification of Functioning, Disability and HealthMedicine

Abstract

fetched live from OpenAlex

OBJECTIVES: (1) To examine the relationships between measures of impairment and disability for the arm and leg with a measure of handicap and (2) to identify the impairment or disability most strongly related to handicap situations. DESIGN: Prospective cohort study. SETTING: Intensive functional rehabilitation unit and community. PATIENTS: One hundred and two persons who had a stroke. MAIN OUTCOME MEASURES: Arm and leg impairments and disabilities were evaluated with reliable and valid tests at discharge from rehabilitation. Six months later, handicap situations were evaluated with the Assessments of Life Habits (LIFE-H). RESULTS: Arm and leg impairments and disabilities are correlated with handicap situations. Disability of the leg is more strongly associated with handicap than arm disability. Arm and leg disabilities are not statistically more strongly related to handicap than arm and leg impairments. CONCLUSIONS: The high correlations found between handicap situations and the impairment and disability measures of the leg provide new information that support the importance of mobility to promote integration after stroke.

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)0.034
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.032
Threshold uncertainty score0.974

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.034
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.019
GPT teacher head0.350
Teacher spread0.331 · 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.

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

Citations90
Published2003
Admission routes1
Has abstractyes

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