Does Ultrasound Guidance Improve the Effectiveness of Neurotoxin Injections in Patients with Cervical Dystonia? (A Prospective, Partially-Blinded, Clinical Study)
Bibliographic record
Abstract
AIM: The aim of this study was to evaluate the efficacy of ultrasound guidance (US) in the treatment of cervical dystonia (CD) with botulinum neurotoxin type A (BoNT-A) injections in comparison to anatomical landmarks (AL). To date, US is routinely used in many centers, but others deny its usefulness. MATERIALS AND METHODS: Thirty-five patients (12 males, 23 females) with a clinical diagnosis of CD were included in the study. Intramuscular administration of BoNT-A was performed using either US guidance, or with AL, in two separate therapeutic sessions. The efficacy of BoNT-A administration was assessed with the Toronto Western Spasmodic Torticollis Rating Scale (TWSTRS), Tsui modified scale, Craniocervical Dystonia Questionnaire (CDQ-24) and Clinical Global Impression-Improvement scale (CGI-I). Additionally, patients at therapeutic sessions were digitally recorded and evaluated by two blinded and independent raters. RESULTS: A significant decrease in total TWSTRS, severity subscale TWSTRS, Tsui score, and CDQ-24 was found in both the AL and US group; however, in the TWSTRS disability and pain subscales, a significant decrease was found only in the US group. Moreover, US guided treatment also resulted in a greater decrease in TWSTRS, Tsui score and CDQ-24 compared to anatomical landmarks use only. CONCLUSIONS: US guidance might be helpful in improving the results of BoNT-A injections in cervical dystonia, reducing associated pain and disability; however, more studies are needed to evaluate its clinical efficacy.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".