Cross-cultural adaptation and translation of the Constant Murley Score into Arabic
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 it