Impact of Neu-botulinumtoxinA on the Severity and Quality of Life of Cervical Dystonia Patients
Bibliographic record
Abstract
Background: Cervical dystonia (CD) is a debilitating neurological disorder that may gravely affect a patient’s quality of life (QoL). Botulinum toxin treatment has been approved as a first-line treatment for this condition. This study aims to look at the efficacy and impact on the QoL of neu-botulinumtoxinA, a newer and cheaper botulinum toxin type A, in patients with CD. Methods: This is a prospective, open-label, single-arm study. CD patients were recruited and evaluated for severity of CD using the Toronto Western Spasmodic Torticollis Rating Scale (TWSTRS), and for QoL using the Craniocervical Dystonia Questionnaire (CDQ-24), and the 36-item Short Form Health Survey questionnaire (SF-36) at baseline and 6 weeks after injection. Results: Twenty patients were recruited. Significant improvement was shown in part 1 and total TWSTRS score and total CDQ-24 scores. Analysis of individual items of the TWSTRS scale showed significant improvement in rotation, duration of CD, and work ability. Significant improvements in the QoL were also seen in some items of the stigma, emotional wellbeing, and energy/fatigue domains of the CDQ-24 and SF-36 questionnaires. Discussion: Neu-botulinumtoxinA is efficacious in treating CD symptoms and improving QoL of patients with CD. A larger, double-blinded study is needed to study the extent of improvements.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.000 |
| 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".