The Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) in Persian Speaking Patients with Knee Osteoarthritis.
Bibliographic record
Abstract
BACKGROUND: Osteoarthritis of the knee is the most common chronic joint disease that involves middle aged and elderly persons. There are different clinical instruments to quantify the health status of patients with knee osteoarthritis and one example is the WOMAC score that has been translated and adapted into different languages. The purpose of this study was cultural adaptation, validation and reliability testing of the Persian version of the WOMAC index in Iranians with knee osteoarthritis. METHODS: We translated the original WOMAC questionnaire into Persian by the forward and backward technique, and then its psychometric study was done on 169 native Persian speaking patients with knee degenerative joint disease. Mean age of patients was 53.9 years. The SF-36 and KOOS were used to assess construct validity. RESULTS: Reliability testing resulted in a Cronbach's alpha of 0.917, showing the internal consistency of the questionnaire to be a reliable tool. Inter-correlation matrix among different scales of the Persian WOMAC index yielded a highly significant correlation between all subscales including stiffness, pain, and physical function. In terms of validity, Pearson`s correlation coefficient was significant between three domains of the WOMAC with PF, RP, BP, GH, VT, and PCS dimensions of the SF-36 health survey (P<0.005) and KOOS (P<0.0001) . CONCLUSIONS: The Persian WOMAC index is a valid and reliable patient- reported clinical instrument for knee osteoarthritis.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".