P.023 Utility of neurophysiological evaluation in movement disorders clinical practice
Bibliographic record
Abstract
Background: Quantitative and objective neurophysiological assessment can help to define the predominant phenomenology and provide diagnoses with prognostic and therapeutic implications. We evaluated retrospectively the indications and final diagnoses of movement disorder neurophysiological evaluations in a specialized movement disorders centre. Methods: Reports from 2003 to 11/2021 were reviewed. The indications were classified according to predominant phenomenology, and the diagnosis of each study was categorized in subgroups of each phenomenology. Results: A total of 525 reports were evaluated. The mean age of patients was 51 years (range 5 – 89 years), and 50% were women. The most common indication was functional movement disorders (33%), followed by jerky movements (25%), tremor (20%), unsteadiness (6%), stiff person syndrome (4%), and other less common indications (12%). The most prevalent diagnoses were functional movement disorder (37%), followed by tremor (28%), comprising of essential (6%), dystonic (5%), cerebellar (4%), parkinsonian (3%) and other types of tremors (10%); and myoclonus (21%), including cortical (8%), subcortical (3%) and undefined (10%) types. Conclusions: This 17-year experience showed that neurophysiological testing can help in the diagnosis of movement disorders. More standardized techniques will encourage the widespread use of neurophysiology to evaluate movement disorders.
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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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".