Cross-cultural adaptation and translation of the Constant Murley Score into Arabic
Bibliographic record
Abstract
INTRODUCTION: Shoulder pain is a major disorder of the musculoskeletal system. To the best of our knowledge, there is no documentation of an Arabic version of the shoulder disability and pain measurements. Constant Murley Score (CMS) is one of the standard questionnaires for clinical practice and research. The aim of this research centred around the evaluation of the Arabic Constant Murley Score and subsequently assessing the reliability and validity in comparison to disabilities of the arm, shoulder, and hand (DASH). METHODS: Hundred and twenty five patients took part in this research. We did the internal consistency tests with Cronbach's alpha. Intra-correlation coefficient, convergent validity, convergent construct validity, responsiveness, and floor and ceiling effects were also calculated. RESULTS: Principal component analysis showed that the variance was 63.31% with a factor range of 0.42-0.85, which fulfils the uni-dimensionality criterion. Also, the Arabic CMS correlated negatively with the DASH score (-0.82, p < 0.001). The Arabic version of CMS was consistent with Cronbach's alpha of 0.74. With Inter Class Correlation Coefficient (ICC) = 0.83 it also showed a very good test-retest reliability. CONCLUSION: Ours is the first translation and cross-cultural adaptation of the CMS into Arabic. Important evidences of validity were tested such as uni-dimensionality, convergent validity, and internal consistency. Results demonstrate an acceptable Cronbach's alpha of 0.74, ICC = 0.830 indicating excellent reliability and a strong correlation of the Arabic CMS with the DASH score (r = -0.820). Overall, the Arabic version of CMS is a good and reliable diagnostic tool for patients experiencing shoulder pain.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".