Effects of A 3-Month Physiotherapy Intervention in Stable Cervical Dystonia As Add-On to Botulinum Toxin Therapy
Bibliographic record
Abstract
Abstract Background Treatment of choice for cervical dystonia comprises botulinum neurotoxin therapy. Whether additional physiotherapy can achieve better results is unclear. Objectives To show effects of physiotherapy as an add-on treatment to botulinum neurotoxin therapy. Methods 18 patients participated in this prospective interventional study (DRKS00006562) consisting of 2, 3-month periods. In the first period, usual botulinum toxin was applied. The second period comprised an additional multimodal physiotherapy program. Outcome measures were: range of motion, SF-36, Toronto Western Spasmodic Torticollis Rating Scale (TWSTRS), surface electromyography. Results In the comparison first phase vs. second phase patients showed significant improvements in range of motion (flexion/extension: 16.3 ± 25.4; rotation: 19.1 ± 24.6), TWSTRS (severity: 3.8 ± 3.0; disability: 1.7 ± 2.0; pain: 4.4 ± 4,1) and in mental health (SF-36: 5.9 ± 7.5); p <0.05, respectively. No significant changes were seen in the lateral flexion ROM, physical health (SF-36) and the surface electromyography. Conclusion Multimodal physiotherapy is a feasible and save additional treatment option for patients with cervical dystonia. An add-on physiotherapy program can achieve better treatment outcomes. Patients benefited especially with improved range of motion, and reduced pain and severity of symptoms.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".