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Test–retest Reliability of Lower Extremity Functional and Self-reported Measures in Elderly with Osteoarthritis

2003· article· en· W2156428069 on OpenAlexaboutno aff
Rachel Davey, Sarah Edwards, Tom Cochrane

Bibliographic record

VenueAdvances in Physiotherapy · 2003
Typearticle
Languageen
FieldEngineering
TopicLower Extremity Biomechanics and Pathologies
Canadian institutionsnot available
Fundersnot available
KeywordsWOMACPhysical therapyOsteoarthritisMedicineReliability (semiconductor)Physical medicine and rehabilitationHamstringStandard errorRepeated measures design

Abstract

fetched live from OpenAlex

The aim of this study was to determine the test–retest reliability for a battery of lower limb functional and self-reported health measures [the Short Form 36 (SF-36) and Western Ontario and McMaster University OA Index (WOMAC)] in elderly with osteoarthritis (OA) of the hip and/or knee. A repeated-measures design was used, in which 21 patients repeated the same tests on two occasions less than 1 week apart. The physical function tests – 8-foot walking distance, stair ascend/descend and right quadriceps strength – demonstrated satisfactory reproducibility with percentage differences on retest ranging from 4% to 7%. The standard error of the measurement (SEM) indicated that these tests demonstrated moderate to good reliability. The hamstring flexor strength measures were unreliable with retest differences of between 16% and 22%. The dimensions of pain and physical function of the WOMAC and six of the eight SF-36 subscales met the standards required for comparing groups of patients. However, the scales measuring role limitation (mental and physical) of the SF-36 showed poor measurement characteristics with a 44% and 201% difference on retest, respectively. The SF-36 physical function and general health subscales together with the pain and physical function dimensions of the WOMAC may be suitable for use as patient self-assessment measures in conjunction with the timed walk and stair test for elderly with lower limb OA.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.234
Threshold uncertainty score0.536

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 head0.007
GPT teacher head0.224
Teacher spread0.217 · 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

Citations22
Published2003
Admission routes1
Has abstractyes

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