Cognitive impairment in Chinese patients with cervical dystonia
Bibliographic record
Abstract
Objective Cognitive impairment (CI) in patients with cervical dystonia (CD) has been reported in many studies but with inconsistent findings. We investigated the prevalence, characteristics, and clinical factors related to CI in Chinese patients with CD. Methods Sixty-eight patients with CD and 68 healthy controls (HCs) were included in the study. Demographic and clinical data were investigated. A logistic regression analysis was conducted to discriminate the clinical factors associated with CI in patients with CD. A cluster analysis was performed to explore the different characteristics within the group of CD patients with CI. Results We found that 42 (61.76%) patients with CD had CI. The most frequent CI domain was visuospatial function (39.71%), followed by memory (38.24%), attention/working memory (29.41%), language (25.00%), and executive function (23.53%). CD patients with CI were older, less educated, had an older age of onset, more severe motor symptoms and disability, and experienced more pain than CD patients without CI. The presence of CI in patients with CD was associated with less education (OR = 0.802, p = 0.034) and a higher Toronto Western Spasmodic Torticollis Rating Scale (TWSTRS) severity subscore (OR = 1.305, p = 0.001). The cluster analysis identified two different subgroups of patients, one with relatively mild cognitive impairment and the other with relatively severe cognitive impairment. Conclusion CI is relatively common in Chinese patients with CD, with the most common CI domain of the visuospatial function. In the present study, CI in patients with CD was associated with less education and more severe motor symptoms, and patients with CI may be further divided into two subgroups based on different extent and domain of cognitive decline.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 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".