KinesioTaping after botulinum toxin type A for cervical dystonia in adult patients
Bibliographic record
Abstract
INTRODUCTION: Studies explored physiotherapeutic approaches in cervical dystonia (CD) patients with or without botulinum toxin (BoNT) injections, however the results are varying. There are no clinical trials investigating the effects of kinesiology taping in CD patients. The objective of this study is to investigate the efficacy of kinesiology taping as an adjunct to the BoNT injections in patients with CD. METHODS: Twenty-five patients were enrolled to the study. Patients were randomly assigned to the experimental 1 (BoNT + KinesioTaping), experimental 2 (BoNT + ShamTaping) or control (BoNT) treatment. After 12 weeks they were moved to the next experimental group and finally every patient received all 3 proposed treatment options. The severity of CD was quantified with the Toronto Western Spasmodic Torticollis Rating Scale (TWSTRS) including Torticollis severity, Disability, and Pain scales. Quality of life was evaluated using Craniocervical dystonia questionnaire (CDQ4). RESULTS: In all treatment groups, there was a significant improvement in dystonia symptoms measured with TWSTRS (total score) after BoNT injection regardless of the allocation to the experimental treatment (p < .05). ANOVA analysis revealed no differences in any of the TWSTRS variables after the intervention. Quality of life was significantly improved after application of taping (p < .05, p = .03). CONCLUSIONS: Application of KinesioTaping after BoNT injection provided no additional effect on the severity of dystonia, although the quality of life was improved in patients with CD. Further research investigating the effect of KinesioTaping prior to BoNT injection is required.
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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 | 0.000 |
| 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 | 0.000 |
| 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".