Cross Cultural Adaptation, Validity and Reliability Test of the Arabic Version of Rotator Cuff Quality of Life Questionnaire
Bibliographic record
Abstract
Background: Shoulder pain is a common problem in orthopedic clinics. The Rotator Cuff Quality of Life Index (RC-QOL) questionnaire covers pain, frequency, severity, symptoms, quality of life, and activities of daily living affected by shoulder disorders and has no Arabic version. Purpose: To find out the validity and reliability of the translated version of RC-QOL among the Arab population in Egypt. Methods: The questionnaire translated into Arabic according to recent guidelines by Tsang. Four experts panels and 340 patients with rotator cuff disorders (RCD) participated in this study. Test-retest was measured by interclass correlation (ICC) with confidence interval (CI) 95% and internal consistency analysis were measured by Cronbach’s alpha value to test reliability. Face, content, and construct validity were evaluated for the RC-QOL. Factor analysis and internal construct validity were assessed, and convergent and divergent validity were tested by the correlation between RC-QOL, Disabilities of The Arm, Shoulder and Hand (DASH) and Western Ontario Shoulder Instability Index (WOSI) questionnaires. Results: The study showed that the scale index of clarity was 90% to 100% and the mean index of clarity was 98.82%, the item content validity index (CVI) ranged from 0.7 to 1, the scale CVI (S-CVI) was 0.97 (97%), the experts proportion of relevance ranged from 91.18 to 100% and the mean was 97.94%. The questionnaire items were filled out 100% on all sheets and it needed less than 13 min maximum and a minimum 3 min to be answered. Cronbach’s alpha was 0.998, ICC was 0.928 to 0.953 with 95%. Conclusion: The Arabic version of the RC-QOL questionnaire is a reliable and valid tool to assess Rotator Cuff Disorder in the Egyptian population.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".