Global Perceptions and Utilization of Clinical Neurophysiology in Movement Disorders
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Clinical neurophysiology (CNP) involves the use of neurophysiological techniques to make an accurate clinical diagnosis, to quantify the severity, and to measure the treatment response. Despite several studies showing CNP to be a useful diagnostic tool in Movement Disorders (MD), its more widespread utilization in clinical practice has been limited. OBJECTIVES: To better understand the current availability, global perceptions, and challenges for implementation of diagnostic CNP in the clinical practice of MD. METHODS: The International Parkinson and Movement Disorders Society (IPMDS) formed a Task Force on CNP. The Task Force distributed an online survey via email to all the members of the IPMDS between August 5 and 30, 2021. Descriptive statistics were used for analysis of the survey results. Some results are presented by IPMDS geographical sections namely PanAmerican (PAS), European (ES), African (AFR), Asian and Oceanian (AOS). RESULTS: Four hundred and ninety-one IPMDS members (52% males), from 196 countries, responded. The majority of responders from the AFR (65%) and PAS (63%) sections had no formal training in diagnostic CNP (40% for AOS and 37% for ES). The most commonly used techniques are electroencephalography (EEG) (72%) followed by surface EMG (71%). The majority of responders think that CNP is somewhat valuable or very valuable in the assessment of MD. All the sections identified "lack of training" as one of the biggest challenges for diagnostic CNP studies in MD. CONCLUSIONS: CNP is perceived to be a useful diagnostic tool in MD. Several challenges were identified that prevent widespread utilization of CNP in MD.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 it