Personalized Bilateral Upper Limb Essential Tremor Therapy with Botulinum Toxin Using Kinematics
Bibliographic record
Abstract
Variability of multi-joint essential tremor (ET) between patients and within the two upper limbs makes a visual assessment for the determination of botulinum toxin type A (BoNT-A) injections challenging. Kinematic tremor analysis guidance has succeeded in overcoming this challenge by making effective long-term unilateral BoNT-A injections for disabling ET. In this open-label study, 31 ET participants received three bilateral arm BoNT-A injection cycles over 30 weeks with follow-ups six-weeks post-treatment. Whole-arm kinematic assessment of tremor using a customized, automated algorithm provided muscle selection and dosing per muscle without clinician’s assessment. Efficacy endpoints included Fahn-Tolosa-Marin tremor scale, quality of life (QoL) questionnaire, and maximum grip strength. BoNT-A reduced tremor amplitude by 47.7% in both the arms at week-6 (p < 0.005) that persisted from weeks 18–30. QoL was improved by 26.5% (p < 0.005) over the treatment period. Functional interference due to tremor was reduced by 30% (p < 0.005) from weeks 6–30. Maximum grip strength was reduced at week 6 (p = 0.001) but was not functionally impaired for the participants. Effective bilateral ET therapy by personalized BoNT-A injections could be achieved using computer-assisted tremor analysis. By removing variability inherent within the clinical assessments, this standardized tremor analysis method enabled patients to have improved bimanual upper limb functionality after the first treatment.
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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) | 0.000 | 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) | 0.001 | 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".