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Record W2018631444 · doi:10.1002/art.20695

Longitudinal comparison of the Health Assessment Questionnaire (HAQ) and the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC)

2004· article· en· W2018631444 on OpenAlexaboutno aff
Bonnie Bruce, James F. Fries

Bibliographic record

VenueArthritis Care & Research · 2004
Typearticle
Languageen
FieldMedicine
TopicOsteoarthritis Treatment and Mechanisms
Canadian institutionsnot available
FundersNational Institute of Arthritis and Musculoskeletal and Skin DiseasesNational Institute on Aging
KeywordsWOMACMedicineOsteoarthritisPhysical therapyGeneralizability theoryHealth assessmentPsychologyAlternative medicinePathology

Abstract

fetched live from OpenAlex

OBJECTIVE: To compare the measurement properties of the generic Health Assessment Questionnaire (HAQ) and the disease-specific Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC). METHODS: Physical function, pain, and radiographic progression were assessed in knee or hip osteoarthritis patients (n = 271) who had 2 radiographs that were at least 6 months apart from 6 ARAMIS (Arthritis, Rheumatism, and Aging Medical Information System) databanks. Data were compared at baseline and after a mean of 3.2 (SE 0.10) years. Correlation coefficients and standardized effect sizes (SES) were used to assess their relationship and responsiveness. RESULTS: The majority of items in the 2 function and pain scales overlapped and were highly and significantly correlated with each other at baseline and last assessments (function at baseline rs = 0.71 and function at last assessment rs = 0.79, P < 0.0001; pain at baseline rs = 0.70 and pain at last assessment rs = 0.76, P < 0.0001). The HAQ disability index and total knee score were more sensitive to detection of disease progression than the WOMAC (SES for HAQ = 0.27; SES for WOMAC = -0.05). CONCLUSION: Both instruments showed favorable measurement properties, with the HAQ having the advantages of being more sensitive to change and adaptable to a wide variety of diseases and conditions, which contribute to the generalizability of findings.

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)0.001
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 score0.348
Threshold uncertainty score0.990

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
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 head0.033
GPT teacher head0.357
Teacher spread0.324 · 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

Citations56
Published2004
Admission routes1
Has abstractyes

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