Isolated Cervical Dystonia: Management and Barriers to Care
Bibliographic record
Abstract
Background: Cervical dystonia (CD) is a rare disorder, and health care providers might be unfamiliar with its presentation, thus leading to delay in the initial diagnosis. The lack of awareness displays the need to highlight the clinical features and treatment in cervical dystonia. In our cohort, we have identified an earlier age of onset in men, despite an overall preponderance of affected women. Objective: We aim to identify the prevalence, age of onset, spread, and treatment modalities of CD in the population. We also highlight the barriers which patients encounter related to diagnosis, follow-up, and treatment. Methods: We reviewed 149 CD patients who attended specialized Dystonia Clinics over a 14-year period. Dystonia severity was rated using the Burke-Fahn-Marsden (BFM), Tsui, and Toronto Western Spasmodic Torticollis Rating Scales (TWSTRS). Mood and quality of life were assessed using Beck Depression Inventory (BDI), Beck Anxiety Inventory (BAI), and 36-Item Short Form Health Survey (SF-36). Results: CD patients were majority White (91.3%) and more commonly female (75.8%). Men had an earlier median age of onset, 40.5 years ( p = 0.044). BAI revealed a mean score of 7.2 (±6.4, n = 50) indicating minimal anxiety while BDI revealed a mean score of 7.30 (±7.6, n = 50) indicating minimal depression. The only SF-36 subscales associated with CD severity were physical functioning ( p = 0.040) pain ( p = 0.008) and general health ( p = 0.014). Conclusion: There appear to be gender differences in both the prevalence and age of onset of the disease. There was a 3-fold higher incidence in women than in men. CD patients of both sexes experience barriers to care, which can be reflected in their quality of life and time-to-diagnosis. In addition, males were less likely to experience an objective benefit with botulinum toxin treatment and more likely to discontinue care. Greater awareness of CD by health care providers is important to reduce the time-to-diagnosis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".