Übersetzung und psychometrische Austestung des Western-Ontario-Rotator-Cuff-Index (WORC) für den Gebrauch in deutscher Sprache
Bibliographic record
Abstract
AIM: At the moment a specific subjective measurement tool for evaluation of German-speaking patients with a pathology of the rotator cuff is lacking. Following international guidelines, the German translation and the psychometric testing of the 21-item, multidimensional Western Ontario Rotator Cuff Index (WORC) is the aim of the study. METHOD: After translation and cross-culture adaptation of the English original, the reliability, validity, practicability and the acceptance of the German version of the WORC were tested on 102 patients with an impingement syndrome. Additionally the SF-36, the Constant and UCLA score were evaluated. RESULTS: The Pearson correlation coefficient showed with 0.96 an excellent result for the test-retest reliability. The internal consistency showed a high homogeneity with a Cronbach alpha coefficient of 0.96. A Pearson correlation coefficient between 0.66-0.81 registered a high correlation with the physical subscales of the SF-36, the Constant and the UCLA score. The mean time required for filling out the WORC was 7.5 minutes, the mean time required for evaluation was 10 minutes. The acceptance and the understanding were very high. CONCLUSION: After successful translation and psychometric testing of the German version of the Western Ontario Rotator Cuff Index (WORC), a patient-based measurement tool for evaluating the quality of life of German-speaking patients with pathology of the rotator cuff is available.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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 | 0.001 |
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".