CROSS CULTURAL ADAPTION, VALIDITY AND RELIABILITY OF URDU VERSIONS OF WOMAC INDEX FOR KNEE OSTEOARTHRITIS INDEX
Bibliographic record
Abstract
Objective: The aim of this study was to translate and cross culturally adapt Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) from source language, English to target language, Urdu. Moreover, to establish its internal consistency, test-retest reliability and validity among knee osteoarthritis patients. Methods: WOMAC was first translated from English to Urdu as per international standardized guidelines. The synthesized version of WOMAC Urdu was initially tested in 12 patients. Final established WOMAC Urdu was administered to 120 knee osteoarthritis patients two times with a 48-hour gap between. IBM Statistics Software SPSS 20.0 was used to analyze scores. Results: Results showed an excellent internal consistency Cronbach’s Alpha ranging from 0.816 to 0.920 for subscales of pain, stiffness and physical function. Intraclass coefficient was ranging from 0.769 to 0.945, Spearman Correlation 0.841 to 0.844 with significant correlation 0.027. There was no ceiling or floor effect with 100% kappa agreement. An excellent content validity was exhibited by significant difference of score with changing severity of knee osteoarthritis. Conclusion: The findings conclude that WOMAC Urdu cross culturally adapted and found to be valid and reliable outcome measure for knee osteoarthritis in Urdu speaking patients. Keywords: WOMAC Urdu, Knee Osteoarthritis, Cross Cultural Adaptation, Health Status, Reliability
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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 | 0.015 |
| 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 | 0.000 |
| 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".