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Record W2157012802 · doi:10.1177/0269215506070792

Reliability of maximal static strength measurements of the arms in subjects with hemiparesis

2007· article· en· W2157012802 on OpenAlexaff
Anne Martine Bertrand, Catherine Mercier, Daniel Bourbonnais, Johanne Desrosiers, Denis Gravel

Bibliographic record

VenueClinical Rehabilitation · 2007
Typearticle
Languageen
FieldMedicine
TopicSports injuries and prevention
Canadian institutionsUniversité de SherbrookeUniversité de MontréalCentre for Interdisciplinary Research in Rehabilitation
Fundersnot available
KeywordsHemiparesisReliability (semiconductor)Physical medicine and rehabilitationPhysical therapyPsychologyMedicinePhysicsSurgery

Abstract

fetched live from OpenAlex

OBJECTIVE: To evaluate the reliability of maximal static strength measurements of five arm muscle groups and of strength ratios (paretic/non-paretic) in subjects with poststroke hemiparesis. DESIGN: The generalizability theory was used to estimate the reliability coefficients and standard errors of measurement of maximal strength for various combinations of trials and sessions and of the strength ratios for one and two sessions. Grip maximal voluntary force and/or maximal voluntary torques exerted by flexor and extensor muscles at both the elbow and shoulder joints were measured in 17 subjects with poststroke hemiparesis. Multiple trials were performed by subjects during two sessions. SETTING: Rehabilitation centre. SUBJECTS: A convenience sample of 17 subjects with poststroke hemiparesis. RESULTS: The reliability coefficients for the strength measurements were in the range of 0.81-0.97 with standard errors of measurement accounting for 4% to 20% of the group means. For the strength ratios, the coefficients of generalizability ranged from 0.76 to 0.95 with standard errors of measurement equal to 6% to 19% of the group means. CONCLUSIONS: The maximal strength measurements of the arms in subjects with hemiparesis are reliable. The strength ratios are also reliable and can be used to quantify strength impairment.

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)0.004
metaresearch head score (Gemma)0.007
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 score0.012
Threshold uncertainty score0.847

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.007
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 head0.038
GPT teacher head0.369
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.

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

Citations61
Published2007
Admission routes1
Has abstractyes

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