Cross-cultural adaptation and validation of the 12-item short forms of the knee injury and osteoarthritis outcome score (KOOS-12) to Persian language
Bibliographic record
Abstract
BACKGROUND: The aim of this study was to translate, culturally adapt, and validate the 12-item short form of the knee injury and osteoarthritis outcome score (KOOS-12) for use in the Persian language. METHODS: This study employed a cross-sectional design involving 105 participants with moderate to severe knee osteoarthritis (OA). The Persian version of the KOOS-12 was administered to assess its test-retest reliability, internal consistency, and construct validity. Test-retest reliability was evaluated using the intraclass correlation coefficient (ICC), with values above 0.75 indicating excellent reliability. Internal consistency was assessed using Cronbach's alpha, where values between 0.70 and 0.90 indicate good to excellent consistency. Construct validity was examined through Pearson correlation coefficients, comparing KOOS-12 scores with established measures such as the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), cincinnati knee rating system (CKRS), Oxford knee score (OKS), and knee society knee scoring system (KSS). Additionally, the standard error of measurement (SEM) and minimal detectable change (MDC) were calculated to provide insights into the measurement's precision and responsiveness, with SEM calculated as SD × √(1-R) and MDC as 1.96 × √2 × SEM. RESULTS: The Persian version of the KOOS-12 was administered to assess its construct validity, test-retest reliability, and internal consistency. Construct validity was evaluated through Pearson correlation coefficients, revealing strong correlations with the KOOS (r = 0.81), moderate correlation with the CKRS (r = 0.56), and strong negative correlation with the WOMAC (r = - 0.77), along with strong correlations with the OKS (r = 0.71) and the KSS (r = 0.73). Test-retest reliability was assessed using the ICC, with values above 0.75 indicating excellent reliability. Internal consistency was measured using Cronbach's alpha, where values between 0.70 and 0.90 indicate good to excellent consistency. CONCLUSION: The analysis of the Persian KOOS-12 questionnaire confirms its excellent reliability, validity, and suitability for clinical use, supported by excellent internal consistency and excellent test-retest reliability. These findings underscore the importance of culturally adapted instruments and indicate the need for future research to evaluate the KOOS-12's responsiveness to clinical changes over time.
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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 | 0.011 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".