Cross-cultural adaptation and psychometric analysis of the Arabic version of the oxford knee score in adult male with knee osteoarthritis
Bibliographic record
Abstract
There are varieties of self-assessment questionnaire used for the evaluation of pain, functional disability, and health related quality of life in individuals with knee osteoarthritis (OA). The present study intended to adapt and translate the oxford knee score into the Arabic and investigated its psychometric properties in adult male with knee OA. Ninety-seven adult male (mean age 57.55 ± 11.49 years) with knee OA participated. Patients were requested to complete the adapted Arabic version of the Oxford knee score (OKS-Ar), reduced “Western Ontario and McMaster Universities Index (WOMAC)”, and the Visual analogue scale (VAS). Patients were requested to complete 2 nd form of OKS-Ar at least 1 week apart to assess the reproducibility of the score. The OKS was adapted and translated into Arabic by two independent Arabic native speakers (one rehabilitation professional having experience of knee OA patients and another one a trained translator) according to the international guidelines. All the participants completed the 2 nd form of OKS-Ar (Response rate 100%). Reliability and internal consistency was high with an ICC of 0.97, and the Cronbach’s alpha coefficient of 0.987, respectively. A significant relationship between the OKS-Ar and the WOMAC and VAS scores confirmed the construct validity ( p < 0.001). The standard error of measurement (SEM) and the minimum detectable change (MDC) were 2.2 and 6.2, respectively. The adapted Arabic version of the OKS demonstrated acceptable psychometric properties, including reliability, internal consistency, and the validity. The present study indicates that the OKS-Ar is a suitable questionnaire to measure pain and physical function in the Arabic speaking adult male patients with knee OA.
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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 | 0.005 | 0.012 |
| 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 | 0.001 |
| 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".