The Danish version of the Western Ontario Rotator Cuff Index.
Bibliographic record
Abstract
INTRODUCTION: The aim of this study was to cross-culturally adapt the Western Ontario Rotator Cuff Index (WORC) into a Danish version (D-WORC) and evaluate its validity, reliability and responsiveness in patients undergoing surgery for arthroscopic subacromial decompression or rotator cuff repair. METHODS: The original WORC version was cross-culturally adapted into Danish and, the validity, test-retest reliability, responsiveness construct validity, internal consistency, interclass correlation coefficient (ICC), limits of agreement (LOA) and an anchor minimal important change (MIC) were assessed using the Disabilities of Arm, Shoulder and Hand (DASH), the Oxford Shoulder Score (OSS), the Short Form-36 and the global rating scale. RESULTS: The cross-cultural adaption was successful. The correlation was high between the D-WORC and DASH (Pearson's correlation coefficient (PCC) = 0.71; 95% confidence interval (CI): 0.60-0.79) and moderate between the D-WORC and the OSS (PCC = 0.67; 95% CI: 0.55-0.76). Reliability analysis showed an ICC of 0.80 (95% CI: 0.69-0.87) and an internal consistency of 0.94 (95% CI: 0.92-0.95). The test-retest mean difference was 76.4 (± standard deviation = 201.40). LOA ranged from -318.3 (95% CI: -387.8--248.9) to 471.2 (95% CI: 401.7-540.6) for the total WORC score. The MIC was -211 in the total score. CONCLUSIONS: The D-WORC is a valid, reliable and responsive questionnaire that can be used in Danish populations. FUNDING: Lone Dragnes Brix: Familien Hede Nielsens Fond, Gurli og Hans Engell Friis' Fond, Aase og Ejnar Danielsens Fond, Knud og Edith Eriksons Mindefond, Region Midtjyllands Sundhedsvidenskabelige Forskningsfond. TRIAL REGISTRATION: Danish Data Protection Agency: 1-16-02-653-15.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".