Clinical Utility of Neurophysiologic Classification (and Declassification) of Myoclonus
Bibliographic record
Abstract
BACKGROUND: Movement clinical neurophysiology studies can distinguish myoclonus, tremor, and other jerky movements; however, there has been limited demonstration of their real-world clinical impact. OBJECTIVE: The aim was to investigate movement study utility in clarifying movement classification and guiding patient management. METHOD: A retrospective study of myoclonus-related movement studies was performed. RESULTS: Of 262 patients referred for consideration of myoclonus, 105 (40%) had myoclonus, 156 (59%) had no myoclonus (the commonest alternative classifications were functional jerks and tremor), and 1 was uncertain. An additional 29 studies identified myoclonus without prior clinical suspicion. A total of 119 of 134 (89%) myoclonus patients had a specific neurophysiologic subtype identified, most commonly cortical (64, 54%). Diagnostic differential narrowed in 60% of patients, and a new diagnosis was made in 42 (14%) patients. Medication changes were made in 151 patients (52%), with improvement in 35 of 51 (67%) with follow-up. CONCLUSIONS: Movement studies effectively determined movement classification and identified unsuspected myoclonus, leading to changes in diagnosis and management. © 2024 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.000 | 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 teacher head, 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".