Health-Related Quality of Life in Cervical Dystonia Using EQ-5D-5L: A Large Cross-Sectional Study in China
Bibliographic record
Abstract
Purpose The study aimed to evaluate the health-related quality of life (HRQoL) measured by the five-level EuroQol-5 dimensions (EQ-5D-5L) in patients with cervical dystonia, and to explore the determinants of HRQoL in patients with cervical dystonia. Methods EQ-5D-5L health state profiles were converted into a single aggregated “health utility” score. A calibrated visual analog scale (EQ VAS) was used for self-rating of current health status. Multiple linear regression analysis was used to explore the factors associated with HRQoL in cervical dystonia. Results A total of 333 patients with cervical dystonia were enrolled in the analysis, with an average age of 44.3 years old. The most common impaired dimension of health was anxiety/depression (73.6%), followed by pain/discomfort (68.2%) and usual activities (48%). The median health utility score was 0.80, and the median EQ VAS score was 70.2. Multivariate linear regression analysis indicated that disease duration and the scores of the Hamilton Depression Rating Scale (HDRS), Pittsburgh sleep quality index (PSQI), Toronto Western Spasmodic Torticollis Rating Scale (TWSTRS) Part I, and TWSTRS Part III were associated with the health utility scores. After adjusting other parameters, the TWSTRS Part III score and the HDRS score were significantly associated with the EQ VAS scores (p < 0.05). Conclusion This study evaluated HRQoL in patients with cervical dystonia using the Chinese version of the EQ-5D-5L scale. We found that, besides motor symptoms, non-motor symptoms, including depression, pain, and sleep quality, could be greater determinants of HRQoL in patients with cervical dystonia. Management of non-motor symptoms, therefore, may help improve HRQoL in patients with cervical dystonia.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".