Transitioning from Unilateral to Bilateral Upper Limb Tremor Therapy for Parkinson’s Disease and Essential Tremor Using Botulinum Toxin: Case Series
Bibliographic record
Abstract
Botulinum toxin type A (BoNT-A) injections guided by kinematic analysis for unilateral upper limb essential tremor (ET) and Parkinson's disease (PD) tremor therapy has demonstrated efficacy, improvements in quality of life (QoL) and arm functionality. In this open-label pilot trial, 5 ET and 2 PD participants decided to switch from receiving long-term unilateral arm treatment to now bilateral BoNT-A arm therapy in their other tremulous arm which worsened over time. Injection patterns were based on kinematic analysis. Efficacy endpoints including kinematic analysis, Fahn-Tolosa-Marin tremor rating scale, QoL questionnaire, and maximal grip strength were collected over 2 treatments and 2 follow-up visits totaling 18-weeks. BoNT-A decreased wrist tremor amplitude by 84.6% and 89.6% 6-weeks following the 1st injection in the newly-treated limb in ET and PD participants, respectively. PD participants started with worse QoL but demonstrated an additional improvement in QoL by 29.9% for switching to bilateral treatment, whereas ET participants did not. Left and right arm tremor also did not share commonalities in severity or dose. This preliminary finding suggests trends for transitioning to bilateral therapy and warrants further studies to evaluate efficacy of bilateral tremor BoNT-A therapy in a larger cohort of PD and ET patients.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".