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Record W2111639979 · doi:10.2522/ptj.20060321

Predictive Value of the Western Ontario and McMaster Universities Osteoarthritis Index for the Amount of Physical Activity After Total Hip Arthroplasty

2007· article· en· W2111639979 on OpenAlexaboutno aff
Robert Wagenmakers, Martin Stevens, Inge van den Akker‐Scheek, Wiebren Zijlstra, Johan W. Groothoff

Bibliographic record

VenuePhysical Therapy · 2007
Typearticle
Languageen
FieldMedicine
TopicPhysical Activity and Health
Canadian institutionsnot available
Fundersnot available
KeywordsWOMACPhysical therapyConfidence intervalMedicineOsteoarthritisSquashLogistic regressionOdds ratioInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND AND PURPOSE: Despite the recognized health benefits of physical activity, little is known about the amount of physical activity that patients perform after total hip arthroplasty (THA). To this end, the ability of the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) to predict the amount of physical activity that patients with a THA perform, as measured by the Short Questionnaire to Assess Health-Enhancing Physical Activity (SQUASH), was determined. SUBJECTS AND METHODS: Three hundred sixty-four patients who had a THA returned questionnaires. Pearson correlation coefficients were calculated between scores on the WOMAC and SQUASH. Binary logistic regression modeling was used to determine the extent to which the WOMAC score could predict that patients would meet national and international guidelines for health-enhancing physical activity. RESULTS: Scores on the WOMAC and SQUASH showed a significant, but low, correlation (r=.14-.24). Although the WOMAC score was a significant predictor for meeting national and international guidelines for physical activity, the odds ratio was low (1.022, 95% confidence interval=1.012-1.033) and only 6.9% of the variance could be explained (Nagelkerke r(2)=.069). DISCUSSION AND CONCLUSION: The results suggest that the WOMAC is not suitable for predicting the amount of physical activity after THA, requiring the use of an additional outcome measure.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.828
Threshold uncertainty score0.380

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 studies0.0000.001
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 head0.020
GPT teacher head0.281
Teacher spread0.261 · 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

Citations13
Published2007
Admission routes1
Has abstractyes

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