Effectiveness of BoNT A in Parkinson's Disease Upper Limb Tremor Management
Bibliographic record
Abstract
OBJECTIVE: One the greatest challenges of BoNT A therapy for tremor lies in the complexity and variation of components involved in tremor movement, and the lack of objective measures to determine these components. This 3 month open-label single injection study aims to couple clinician best judgment with kinematics to improve effect of BoNT A (incobotulinumtoxinA) injection in 7 patients with upper limb Parkinson's disease (PD) tremor. METHODS: Injection was guided with clinical and kinematic assessment of tremor using angular wrist position in 3 degrees of freedom: flexion/extension, pronation/supination, and radial/ulnar deviation. Overall tremor severity and change were measured by linear finger acceleration. RESULTS: Kinematic data from static and functional tasks demonstrate no improvement at one month post-injection, but significant improvement at two and three months. Clinical scales across UPDRS Items 20 (1, 2, 3 months post) and 21 (2 months), and spiral drawings (3 months) showed significant improvement from baseline, while line drawings did not. CONCLUSIONS: This study suggests injection of BoNT A as a viable focal management option for upper limb PD tremor. In addition to clinical judgment, objective quantification of tremor dynamics by kinematics may be a feasible assessment and guidance tool which can be used to optimize injection conditions for focal tremor therapy. Kinematic analysis of tremor across a variety of joints in all degrees of movement may provide important insight into tremor dynamics, allowing optimized, targeted focal therapy.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 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".