EMG versus US: a randomized clinical trial comparing the efficacy in guiding botulinum toxin treatment in cervical dystonia
Bibliographic record
Abstract
Abstract Botulinum toxin type A (BoNT-A) is considered the first-line therapy for cervical dystonia. To compare, in a randomized trial, the efficacy of treatment with BoNT-A guided by ultrasound (US) and electromyography (EMG) in patients with idiopathic cervical dystonia. A total of 40 patients (20 in each group; mean age: 54 years; 45% of female subjects; mean disease duration: 10.7 years) were randomized to either US- or EMG-guided BoNT-A treatment. The efficacy of BoNT-A was assessed through changes in the scores on the Toronto Western Spasmodic Torticollis Rating Scale (TWSTRS) before and 4 to 6 weeks after the treatment. The differences in the absolute and relative changes in the total TWSTRS scores and in its components (severity, incapacity, and pain) between the groups were evaluated. The US and EMG groups were well balanced in relation to baseline and demographic characteristics. After the BoNT-A treatment, there was a mean reduction in the TWSTRS score of 8 points (relative reduction of 23%) equally between the US and EMG groups (mean difference in absolute decrease of 0.1 point; p = 0.97; and mean difference in relative decrease of 2%; p = 0.89). There were no differences in the declines in the scores on the TWSTRS components, nor when the improvements in the TWSTRS scores were dichotomized as more significant or lower reductions (all p-values > 0.3). The present randomized trial did not demonstrate any difference in improvements between BoNT-A treatment guided by US or EMG in patients with idiopathic cervical dystonia. ReBEC Identifier: RBR-33dd4p4.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".