Principles of Electrophysiological Assessments for Movement Disorders
Bibliographic record
Abstract
Electrophysiological studies can provide objective and quantifiable assessments of movement disorders. They are useful in the diagnosis of hyperkinetic movement disorders, particularly tremors and myoclonus. The most commonly used measures are surface electromyography (sEMG), electroencephalography (EEG) and accelerometry. Frequency and coherence analyses of sEMG signals may reveal the nature of tremors and the source of the tremors. The effects of voluntary tapping, ballistic movements and weighting of the limbs can help to distinguish between organic and functional tremors. The presence of Bereitschafts-potentials and beta-band desynchronization recorded by EEG before movement onset provide strong evidence for functional movement disorders. EMG burst durations, distributions and muscle recruitment orders may identify and classify myoclonus to cortical, subcortical or spinal origins and help in the diagnosis of functional myoclonus. Organic and functional cervical dystonia can potentially be distinguished by EMG power spectral analysis. Several reflex circuits, such as the long latency reflex, blink reflex and startle reflex, can be elicited with different types of external stimuli and are useful in the assessment of myoclonus, excessive startle and stiff person syndrome. However, limitations of the tests should be recognized, and the results should be interpreted together with clinical observations.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.008 |
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".