The test-retest reliability and correlation of Thai version of the Western Ontario and McMaster Universities Osteoarthritis Index and pain scale in older people with knee osteoarthritis
Bibliographic record
Abstract
The Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) is a standardized questionnaire that is widely the most use by health professionals for evaluate conditions of patient with knee osteoarthritis (OA). The 5-point Likert WOMAC was developed to use among older people with knee OA. Aim of this study was to estimate the test–retest reliability of Thai 5-point Likert version of WOMAC in older people with knee OA and correlation with visual analogue scale (VAS) pain. Thirty older people with symptomatic knee OA aged 50-85 years (average age 68.7±7.4 years) have live in Tumbon Sila, Muang district, Khon Kaen province. All subjects were completely asked to self-reported functional impairment the WOMAC form, 2 times with assistance of investigators. The WOMAC includes 3 dimensions in evaluating of pain, stiffness and physical functioning of the joints. Intraclass correlation coefficient (ICC) was used to describe the test- retest reliability. The ICC of pain dimension = 0.75 (95% CI: 0.47 to 0.88), stiffness dimension = 0.59 (95% CI: 0.14 to 0.81), physical function dimension = 0.81 (95% CI: 0.60 to 0.91) and global = 0.86 (95% CI: 0.70 to 0.93). The significant correlation between WOMAC pain subscale and VAS was moderate (r = 0.43, p<0.05). The study concluded the Thai 5-point Likert version of WOMAC is a suitable tool for assessing knee OA in Thai elderly because of good reliable, low cost and timeless. In using WOMAC evaluated elderly people with OA in the future should consider other impact factors such as context and culture of community. Keywords: Knee osteoarthritis, Reliability, Correlation, WOMAC, VAS
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".