Validation and Cross-Cultural Adaptation of the Hindi Version of the Oxford Knee Score in Patients With Knee Osteoarthritis
Bibliographic record
Abstract
INTRODUCTION: Cases of knee osteoarthritis are on the rise in India with an increasingly ageing population. A large number among them shall undergo total knee replacement, so there is a requirement for validated patient-reported outcome measures in the Hindi language. Oxford Knee Score (OKS) is one of the most commonly used patient-reported outcome measure scoring systems. The current study was designed to test and validate cross-cultural adaptation and translate the Hindi version of the Oxford Knee Score (OKS-H). Material and Methods: The OKS-H was formulated as per recommendations for cross-cultural adaptation and translation. The OKS was tested on 162 patients with knee osteoarthritis who underwent a total knee replacement. Reliability of the OKS-H was tested using the intraclass correlation coefficient (ICC) and internal consistency was assessed using Cronbach's alpha. The construct validity was assessed using OKS-H, Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), and 36-Item Short Form Survey (SF-36) questionnaire. Results: The translation was performed with no major difficulty. The OKS was completed by 158 (97.5%) and 157 (96.9%) patients at test and retest, respectively, after one week. With an ICC of 0.87, OKS had shown good reliability. The construct validity obtained against the WOMAC and SF-36 scores was strong (ICC between 0.49 to 0.86). CONCLUSION: The translated OKS-H is a reliable and valid instrument for patient-reported outcome measures in cases of knee osteoarthritis opting for total knee arthroplasty.
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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 | 0.010 | 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 | 0.000 | 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".