Dynamic Decomposition of Motion in Essential and Parkinsonian Tremor
Bibliographic record
Abstract
BACKGROUND: Treatment options for essential (ET) and Parkinson disease (PD) tremor are suboptimal, with significant side effects. Botulinum toxin type A (BoNT A) is successfully used in management of various focal movement disorders but is not widely used for tremor. METHOD: This study examines complexity of wrist tremor in terms of involvement of its three anatomical degrees of freedom (DOF) in two common situations of rest and posture. The study examines tremor in 11 ET and 17 PD participants by kinematic decomposition of motion in 3-DOF. RESULTS: Tremor decomposition showed the motion involved more than one DOF (<70% contribution in one DOF) in most ET (rest: 100%, posture: 64%) and PD (rest: 77%, posture: 77%) patients. Task variation resulted in change in both amplitude and composition in ET, but not in PD. Amplitude significantly increased from rest to posture in ET. Directional bias was observed at the wrist for ET (pronation), and PD (extension, ulnar deviation, pronation). Average agreement between clinical visual and kinematic selection of muscles was 55% across all subjects. CONCLUSION: This study shows the complexity of tremor and the difficulty in visual judgment of tremor, which may be key to the success of targeted focal treatments such as BoNT A.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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".