Turkish version of the Rotator Cuff Quality of Life questionnaire in rotator cuff‐impaired patients
Bibliographic record
Abstract
PURPOSE: To date, the "Rotator Cuff Quality of Life" (RC-QOL) measure has not been translated into Turkish. The aim of this study was to perform a cross-cultural adaptation of the questionnaire and determine the reliability and reproducibility of the "Turkish version of the RC-QOL" (Tur-RC-QOL) questionnaire on Turkish-speaking patients. METHODS: The translation followed an established forward-and-backward translation procedure. Thirty Turkish-speaking, rotator cuff-impaired patients were enrolled in the study. The validity of the Tur-RC-QOL was assessed and compared with the "Shoulder Pain and Disability Index" (SPADI) and the "Western Ontario Rotator Cuff Index" (WORC) using Pearson's correlation coefficients. A test-retest interval of 2 days was used to assess the reliability. Internal consistency was tested by Cronbach's alpha, relative reliability with "intraclass correlation coefficient" (ICC), and absolute reliability using the formula for the "standard error of measurement" (SEM). RESULTS: The Cronbach's alpha scores were high for the total scores and subheadings of the Tur-RC-QOL, in the range of 0.83-0.98. Excellent test-retest reliability scores were found for the total score and for all parts of the Tur-RC-QOL, with the exception of "Part E". The ICC score for Part E was relatively lower than other parts (ICC = 0.71), and the SEM score was relatively higher (17.92 %). The Pearson correlation coefficients for the Tur-RC-QOL were high for SPADI (r = 0.90, p < 0.001) and WORC (r = 0.85, p < 0.001). CONCLUSIONS: This study demonstrates that the Tur-RC-QOL is a reliable and valid instrument to assess the quality of life of rotator cuff-impaired patients. LEVEL OF EVIDENCE: III.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".