Novel Botulinum Toxin Injection Protocols for Parkinson Tremor and Essential Tremor – the Yale Technique and Sensor-Based Kinematics Procedure for Safe and Effective Treatment
Bibliographic record
Abstract
Background: Hand tremor associated with Parkinson disease (PD) and essential tremor (ET) can often become challenging to treat in clinical practice. Local injections of botulinum toxin-A (BoNT-A) for hand tremor is an evolving field with newer injection techniques being utilized in clinical studies. The utility of BoNT-A therapy for ET and PD-tremor however, has been questioned based on the high incidence of finger and hand weakness after treatment. Method: The study includes detailed analysis of the techniques utilized in BoNT injection in ET and PD tremor. Results: There were 4 high-quality investigations which consisted of Class I or II double-blind placebo-controlled trials and one medium-quality study that was a prospective, open label, class III investigation. Discussion: This paper discusses two recently developed technology-based injection methods for BoNT-A therapy of ET and PD tremor, which includes comprehensive EMG screening of forearm and arm muscles with selective injections (Yale method) and the whole arm kinematic tremor assessment developed by Jog et al. In recent years, controlled, blinded studies of these two methods have shown significant post-injection reduction of finger, hand and whole limb tremor compared to the previously published controlled clinical trials not using these methodologies.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".