Cross-cultural translation of the Lysholm knee score in Chinese and its validation in patients with anterior cruciate ligament injury
Bibliographic record
Abstract
BACKGROUND: The Lysholm Knee Score (LKS) is widely used and is one of the most effective questionnaires employed to assess knee injuries. Although LKS has been translated into multiple languages, there is no Chinese version even though China has the largest population of patients with knee-joint injuries. The objective of our study was to develop the Chinese version of LKS (C-LKS) and assess its reliability, validity and responsiveness in Chinese patients with anterior cruciate ligament (ACL) injuries. METHODS: Study participants were mainly recruited among patients with ACL injuries scheduled for arthroscopic ACL reconstruction at our hospital. First, we developed the C-LKS in a five-step translation and cross-cultural adaptation procedure. Next, we calculated the Cronbach's alpha, intraclass correlation coefficient (ICC), Pearson's correlation coefficient (r), effect size (ES), and standardized response mean (SRM) to evaluate the reliability, validity, and responsiveness of C-LKS respectively. RESULTS: Overall, 126 patients with ACL injuries successfully completed the questionnaires. Acceptable internal consistency (Cronbach's alpha = 0.726) as well as excellent test-retest reliability (ICC = 0.935) was found for C-LKS. Good or moderate correlation (r = 0.514-0.837) was determined among C-LKS and International Knee Documentation Committee Subjective Knee Form (IKDC), Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), physical subscales of SF-36; C-LKS also had fair or moderate correlation (r = 0.207-0.462) with the other subscales of SF-36, which adequately illustrated that good validity was included in C-LKS. In addition, good responsiveness was also observed in C-LKS (ES = 1.36,SRM = 1.26). CONCLUSIONS: We have shown that our developed C-LKS questionnaire is reliable, valid and responsible for the evaluation of Chinese-speaking patients with ACL injuries and it would be an effective 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) | 0.000 | 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".