Sensitization-Associated Symptoms and Neuropathic-like Features in Patients with Cervical Dystonia and Pain
Bibliographic record
Abstract
Background: This exploratory study evaluated the presence of sensitization-associated and neuropathic-like symptoms and identified their association with pressure sensitivity, pain, and disability in patients with cervical dystonia (CD). Methods: Thirty-one patients with CD (74.2% women, age: 61.2 years, SD 10.1) participated. Data collected included clinical variables, the Toronto Western Spasmodic Torticollis Rating Scale (TWSTRS), the Central Sensitization Inventory (CSI), the Self-administered Leeds Assessment of Neuropathic Symptoms and Signs (S-LANSS), the Hospital Anxiety and Depression Scale (HADS) and the Pittsburgh Sleep Quality Index (PSQI), as well as widespread pressure pain thresholds (PPTs). Results: Patients with CD with pain (n = 20, 64.5%) showed higher scores on the TWSTRS disability subscale and the CSI (p < 0.001), and lower PPTs (p < 0.05). Fifteen patients (15/31, 48%) showed sensitization-associated symptoms (CSI ≥ 40), whereas five of the patients with pain (5/20, 25%) exhibited neuropathic-like symptoms (S-LANSS ≥ 12). The CSI and S-LANSS were positively associated with the TWSTRS, HADS-A and HADS-D, and negatively associated with PPTs. HADS-D and S-LANSS explained 72.5% of the variance of the CSI (r2: 0.725), whereas CSI explained 42.3% of the variance of the S-LANSS (r2: 0.423). Conclusions: Pain is an important source of disability in CD, and may be a consequence of different mechanisms, including sensitization.
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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 | 0.000 | 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) | 0.002 | 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".