The responsiveness and validity of the Rotator Cuff Quality of Life (RC-QOL) index in a 2-year follow-up study
Bibliographic record
Abstract
Background: The Rotator Cuff Quality of Life (RC-QOL) index was developed to evaluate quality of life in patients with rotator cuff disease. This study provides additional psychometric testing in accordance with the Consensus-Based Standards for the Selection of Health Measurement Instruments guidelines. Methods: This was a 2-year follow-up study on 66 patients (mean age, 59 ± 10 years) originally presenting with chronic full-thickness rotator cuff tears to a tertiary care center. The methodology involved testing internal consistency, content validity, and criterion validity. Responsiveness was evaluated using 3 strategies: 1) standardized response mean of the raw change scores; 2) Guyatt's Responsiveness Index; and 3) Global Rating Scales of improvement correlated to a quality of life measure. Results: < .001). The effect size of distribution-based methods of determining responsiveness was large (0.99-1.09) compared to that of mixed- and anchor-based methods (0.47-0.89). All responsiveness calculations met minimum requirements for acceptable thresholds. Conclusion: The RC-QOL is a valid and responsive measure of health-related quality of life in patients with chronic rotator cuff pathology. The results of this study added to the methodologic quality assessment of the RC-QOL, completing 7 of 10 Consensus-Based Standards for the Selection of Health Measurement Instruments criteria.
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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 | 0.012 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".