한국판 웨스턴 온타리오 어깨 불안정 지수의 교차문화 적용 및 측정 속성
Bibliographic record
Abstract
Background : Western Ontario Shoulder Instability Index (WOSI) is a disease specific questionnaire to measure quality of life with regards to shoulder instability. This study aimed to enable cross-cultural adaptation of Western Ontario Shoulder Instability Index into Korean and investigate its reliability and validity. Methods : Translation was completed in accordance with the translation and backward translation guideline that development team recommend. The Korean WOSI (K-WOSI) and Korean Quick Disabilities of the Arm, Shoulder and Hand for Koreans (K-QuickDASH) were answered by subjects for reliability and validity test. K-WOSI was filled out again after the first evaluation after mean 2 days. In addition, floor and ceiling effects were evaluated. Results : Total 16 patients waiting for shoulder stabilizing surgery recruited. Internal consistency was high (Cronbach‘s alpha = 0.97 for total score of K-WOSI). Test-retest reliability measured by Pearson’s r and Intraclass Correlation Coefficient for total score were 0.86 and 0.92 respectively, confirming excellent reliability. And total scores of K-WOSI and K-QuickDASH showed high correlation (Pearson’s r = 0.88). Also, there were no floor and ceiling effects observed in the K-WOSI. Conclusions : The study findings confirmed that K-WOSI is a reliable and valid tool that assesses patients‘ quality of life with shoulder instability.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".