Personalized Bilateral Upper Limb Essential Tremor Therapy with Botulinum Toxin Using Kinematics
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.002 | 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 it