Botulinum Toxin Type A Injections as Monotherapy for Upper Limb Essential Tremor Using Kinematics
Bibliographic record
Abstract
BACKGROUND: There is a significant need for a targeted therapy for essential tremor (ET), as medications have not been developed specifically for ET, and the ones prescribed are often not well-tolerated, so that many patients remain untreated. Recent work has shown that, unlike previous experience, kinematically guided individualized botulinum toxin type A (BoNT-A) injections provide benefit along with minimal weakness. Ours is the first long-term (96-week) safety and efficacy study of BoNT-A as monotherapy for ET using kinematically driven injection parameters. METHODS: Ten ET patients were administered six serial BoNT-A treatments every 16 weeks and were assessed at 6 weeks following treatment. During each study visit, the Fahn-Tolosa-Marin (FTM) scale, the Unified Parkinson's Disease Rating Scale, and the Quality of Life for Essential Tremor Questionnaire (QUEST) were administered along with kinematic assessment of the treated limb. Participants performed scripted tasks with motion sensors placed over each arm joint. Dosing patterns were determined using the movement disorder neurologist's interpretation of muscles contributing to the kinematically analyzed upper limb tremor biomechanics. RESULTS: There was a 33.8% (p<0.05) functional improvement (FTM part C) and a 39.8% (p<0.0005) improvement in QUEST score at week 96 compared to pretreatment scores at week 0. Although there was a 44.6% (p<0.0005) non-dose-dependent reduction in maximal grip strength, only 2 participants complained of mild weakness. Following the fourth serial treatment, mean action tremor score was reduced by 62.9% (p=0.001) in the treated and by 44.4% (p=0.03) in the untreated arm at week 96 compared to week 48. CONCLUSIONS: Individualized BoNT-A dosing patterns to each individual's tremor biomechanics provided an effective monotherapy for ET as function improved without functionally limiting muscle weakness.
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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) | 0.001 | 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".