Reliability and Internal Consistency of the Spanish Version for Colombia of the Western Ontario Rotator Cuff Index (WORC)
Bibliographic record
Abstract
Background: The Western Ontario Rotator Cuff Index (WORC) is an assessment tool developed to evaluate quality of life in patients with rotator cuff disease (RCD). The purpose of this study is to translate the WORC index into Spanish and to evaluate its reproducibility and internal consistency in patients with RCD. Methods: Following guidelines from literature, the WORC index was translated. Sixty patients with RCD were asked to complete the questionnaire. To evaluate reliability, they were asked to answer it for a second time within the next 14 days. The Cronbach’s α (CA) and the intraclass correlation coefficient (ICC) were calculated to determine test-retest reliability and internal consistency. Bland-Altman plot and reliable change index (RCI) were used to evaluate measurement error. Results: Cronbach’s α was 0.96 for the total WORC score (ranges 0.85-0.94 for the five domains).Excellent test-retest reliability was seen with an ICC of 0.98, with the domains ranging between 0.91-0.97. The Bland-Altman plot showed no systematic differences, and the RCI for the total WORC index was 7.6%. Conclusion: The Spanish version of the WORC index is a valid and reliable tool for evaluating quality of life in patients with RCD and may be used in Spanish speaking countries like Colombia. Level of evidence: Basic Science Study, Development or Validation of Outcomes Instruments/Classification Systems.
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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 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".