Cross-cultural adaption of the Knee injury and Osteoarthritis Outcome Score (KOOS) into Punjabi for knee injury and osteoarthritis patients in Canada
Bibliographic record
Abstract
BACKGROUND: The Knee injury and Osteoarthritis Outcome Score (KOOS) is a knee-specific patient-reported outcome that is used to assess knee-related symptoms, function and quality of life across a variety of knee conditions in patient populations. Currently there is no Punjabi version of the tool available. This study aims to cross-culturally adapt the KOOS tool from the source English language to the target Punjabi language for use in the Canadian health context. METHODS: We followed standard guidelines including: 1) creation of a concept definition document 2) forward translation 3) reconciliation 4) back translation 5) expert committee review 6) creation of pilot version for cognitive interviews 7) cognitive interviews 8) final review and proof reading. RESULTS: Thirty people identifying as South Asian with lived experiences of various knee conditions took part in cognitive interviews (70% women, mean age 61 years) to provide insights into equivalence in conceptual, semantic, and content between the source English language and the target Punjabi language KOOS. Cognitive interviews identified comprehension and interpretation, structural, conceptual, cultural, and other issues in the preliminary Punjabi KOOS. These issues were addressed considering the Punjabi audience and culture in Canada, and the purpose of the tool to arrive at a cross-culturally adapted Punjabi KOOS. CONCLUSION: A cross-culturally Punjabi version of the KOOS is available to assess knee related outcomes of SA Punjabi patients in Canada. Future validation of the tool is required with SA Punjabi patients in Canada to ensure that the "target Punjabi instrument" has the same properties as the "original English KOOS instrument".
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".