A Screening Tool to Quickly Identify Movement Disorders in Patients with Inborn Errors of Metabolism
Bibliographic record
Abstract
BACKGROUND: Movement disorders are frequent in patients with inborn errors of metabolism (IEMs) but poorly recognized, particularly by nonmovement disorder specialists. We propose an easy-to-use clinical screening tool to help recognize movement disorders. OBJECTIVE: The aim is to develop a user-friendly rapid screening tool for nonmovement disorder specialists to detect moderate and severe movement disorders in patients aged ≥4 years with IEMs. METHODS: Videos of 55 patients with different IEMs were scored by experienced movement disorder specialists (n = 12). Inter-rater agreements were determined on the presence and subtype of the movement disorder. Based on ranking and consensus, items were chosen to be incorporated into the screening tool. RESULTS: A movement disorder was rated as present in 80% of the patients, with a moderate inter-rater agreement (κ =0.420, P < 0.001) on the presence of a movement disorder. When considering only moderate and severe movement disorders, the inter-rater agreement increased to almost perfect (κ = 0.900, P < 0.001). Dystonia was most frequently scored (27.3%) as the dominant phenotype. Treatment was mainly suggested for patients with moderate or severe movement disorders. Walking, observations of the arms, and drawing a spiral were found to be the most informative tasks and were included in the screening tool. CONCLUSIONS: We designed a screening tool to recognize movement disorders in patients with IEMs. We propose that this screening tool can contribute to select patients who should be referred to a movement disorder specialist for further evaluation and, if necessary, treatment of the movement disorder. © 2023 The Authors. 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 | 0.001 |
| 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) | 0.000 | 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".