Cross-cultural adaptation and validation for central sensitization inventory: based on Chinese patients undergoing total knee arthroplasty for knee osteoarthritis
Bibliographic record
Abstract
Abstract Background This study was conducted to develop a simplified Chinese version of the central sensitization inventory (CSI-CV) and to evaluate its reliability and validity. Methods The CSI-CV was developed through a process involving the translation and back translation of the original CSI. Subsequently, experts reviewed and revised the content of the items to ensure their appropriateness. A total of 325 patients with knee osteoarthritis (KOA), who were scheduled to undergo total knee arthroplasty (TKA), completed the CSI-CV at a prominent orthopedic center in Xi'an, China. Afterward, a random selection of 100 participants was chosen for retesting after one week. The reliability and validity of the inventory were evaluated through exploratory factor analysis, correlation coefficient calculation and other methods. Results The CSI-CV consists of 25 items in five dimensions (emotional distress, headache and jaw symptoms, physical symptoms, urological symptoms, and fatigue and sleep problems). The cumulative variance contribution rate was 75.3%, the Cronbach's α coefficient was 0.83, the Guttman split-half reliability coefficient was 0.88 and the intraclass correlation coefficient was 0.965. The CSI-CV scores correlated moderately with the total scores of the brief pain inventory (r = 0.506), Western Ontario and McMaster Universities Osteoarthritis Index (r = 0.466) and EuroQoL Group's five-dimension questionnaire (r = 0.576). Conclusions The findings demonstrate that the CSI was successfully trans-culturally adapted into a simplified Chinese version (CSI-CV) that was reliable and valid for Chinese-speaking patients who awaiting TKA for KOA.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".