Real-World Longitudinal Experience of Botulinum Toxin Therapy for Parkinson and Essential Tremor
Bibliographic record
Abstract
Background: Botulinum toxin type A (BoNT-A) therapy for upper-limb tremor has emerged as a promising option. However, it is unclear in real-world practices whether a technology-guided approach can compare with expert clinical assessments (including surface anatomy and palpation) for improving outcomes. This retrospective study aims to review our clinical outcomes of treating essential tremor (ET) and Parkinson’s disease (PD) tremor using either clinical- or kinematic-based injection pattern determination methods. Methods: 68 ET and 45 PD patients received at least one injection for their upper-limb tremor (unilateral or bilateral) in the last 7 years. Demographics of patients and BoNT-A injections were collected. A Mann–Whitney U statistical test was used to compare outcome measures between ET and PD cohorts. Results: Mean age (72 ± 9 years), number of injections (5), years receiving therapy (~2 years), clinic- (~57%) or kinematic-based patterns, and self-paying (52%) were similar between both cohorts. BoNT-A as a monotherapy in both upper limbs was received in more ET than PD patients. Double reconstitution of Xeomin® in the wrist flexors/extensors, supinator, biceps, and triceps were most injected. Discontinuation due to no benefit/weakness was not dependent on the injection pattern determination approach. Conclusions: Kinematic-based BoNT-A injections produced similar treatment outcomes to injections based on the clinical expertise of the expert injector. This suggests that kinematics could be used by a non-expert to attain equivalent efficacy potentially improving access to this 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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".