Cross-cultural adaptation and validation of the Korean version of the Central Sensitization Inventory in patients undergoing total knee arthroplasty for knee osteoarthritis
Bibliographic record
Abstract
The purpose of this study was to establish a Korean version of the Central Sensitization Inventory (CSI-K) for Korean-speaking patients facing total knee arthroplasty (TKA) for knee osteoarthritis (OA) and to investigate the psychometric characteristics of the CSI-K. We recruited a total of 269 patients with knee OA who were scheduled to undergo TKA for the study. CSI-K and pain-related outcomes, including the pain visual analog scale (VAS) and the Western Ontario and McMaster Universities OA Index (WOMAC) pain sub-scores, were measured. Since central sensitization (CS) is closely related to the quality of life (QOL) and limited functionality as well as pain, the patient's function was measured using the WOMAC function sub-scores, and QOL was measured using the EuroQol five-dimension test (EQ-5D). Reliability and validity were evaluated. Exploratory factor analysis (EFA) was conducted to begin the data reduction to validate the existing questionnaire translation. The internal consistency was excellent, with a Cronbach's alpha of 0.941. The test-retest reliability was acceptable-to-excellent with an ICC of 0.888. As expected, the CSI scores correlated strongly with the WOMAC pain scores (r = 0.524, p < 0.001) and moderately with the pain VAS (r = 0.496, p < 0.001), the WOMAC function (r = 0.408, p < 0.001), and the EQ-5D scores (r = 0.437, p < 0.001). EFA resulted in a six-factor model. The findings demonstrate that the CSI was successfully trans-culturally adapted into a simplified Korean version (CSI-K) that was reliable and valid for Korean-speaking patients who awaiting TKA for knee OA.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.000 |
| 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".