Clinical and demographic characteristics related to onset site and spread of cervical dystonia
Bibliographic record
Abstract
BACKGROUND: Clinical characteristics of isolated idiopathic cervical dystonia such as onset site and spread to and from additional body regions have been addressed in single-site studies with limited data and incomplete or variable dissociation of focal and segmental subtypes. The objectives of this study were to characterize the clinical characteristics and demographics of isolated idiopathic cervical dystonia in the largest standardized multicenter cohort. METHODS: The Dystonia Coalition, through a consortium of 37 recruiting sites in North America, Europe, and Australia, recruited 1477 participants with focal (60.7%) or segmental (39.3%) cervical dystonia on examination. Clinical and demographic characteristics were evaluated in terms of the body region of dystonia onset and spread. RESULTS: Site of dystonia onset was: (1) focal neck only (78.5%), (2) focal onset elsewhere with later segmental spread to neck (13.3%), and (3) segmental onset with initial neck involvement (8.2%). Frequency of spread from focal cervical to segmental dystonia (22.8%) was consistent with prior reports, but frequency of segmental onset with initial neck involvement was substantially higher than the 3% previously reported. Cervical dystonia with focal neck onset, more than other subtypes, was associated with spread and tremor of any type. Sensory tricks were less frequent in cervical dystonia with segmental components, and segmental cervical onset occurred at an older age. CONCLUSIONS: Subgroups had modest but significant differences in the clinical characteristics that may represent different clinical entities or pathophysiologic subtypes. These findings are critical for design and implementation of studies to describe, treat, or modify disease progression in idiopathic isolated cervical dystonia. © 2016 International Parkinson and Movement Disorder Society.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".