Translation, validation and cross‐cultural adaptation of the Nepali version of WOMAC<sup>®</sup> LK 3.1
Bibliographic record
Abstract
BACKGROUND: Among many self-reported outcome measures, Western Ontario and McMaster Universities Arthritis Index (WOMAC) is one of the commonly used indexes for osteoarthritis patients. Despite high prevalence of musculoskeletal problems in Nepal, there is no validated tool available in the local language. Thus, this study aimed to translate the English WOMAC® into Nepali, and validate it for use in the future. METHODS: Guidelines by Beaton et al were followed for translation and cross-cultural adaptation of the original WOMAC into Nepali language. Diagnosed cases of knee osteoarthritis (OA) attending the rheumatology outpatient department of National Center for Rheumatic Diseases, Kathmandu were enrolled in the study. These patients were interviewed with the Nepali version. Internal consistency was measured by Cronbach's alpha and test-retest reliability by intra-class correlation coefficient (ICC). Correlation between domains of WOMAC was tested with visual analog scale (VAS) and numerical rating scale (NRS) of pain and stiffness. RESULTS: The test-retest reliability was good with ICC of >0.75 for all domains and items. Internal consistency was acceptable with Cronbach's alpha scores of 0.852, 0.704 and 0.955 for pain, stiffness and physical function domains, respectively. Strong correlation was observed between WOMAC stiffness domain and VAS for stiffness and NRS for stiffness with rho (ρ) values of 0.750 and 0.759, respectively. Moderate correlation was seen between WOMAC pain and VAS for pain and NRS for pain with ρ of 0.658 and 0.584, respectively. CONCLUSIONS: Nepali WOMAC is a reliable and valid instrument for assessment of disease severity and its impact in Nepali-speaking patients with 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 | 0.015 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".