Non‐motor Symptom Scales in Pediatric Movement Disorders: A Call for Diagnostic‐Specific Tools
Bibliographic record
Abstract
The importance of considering non-motor symptoms (NMS) in the assessment of patients with movement disorders is widely recognized.1 In adults, symptoms such as pain, sleep disturbances, anxiety, fatigue, and cognitive dysfunction can be systematically evaluated, sometimes with validated, condition-specific scales.2, 3 For example, the Pain in Dystonia Scale (PIDS) has been recently developed for pain assessment across the spectrum of adult-onset isolated dystonia.4 In children, however, the evaluation of NMS remains inconsistent, fragmented, and poorly standardized.1 Yet, NMS are often disabling and have substantial consequences for the quality of life of children with movement disorders and their families.5 In a recent scoping review,6 we provide a timely overview of NMS scales used in children with movement disorders, focusing on the three most prevalent conditions, namely dystonia, tics, and cerebral palsy (CP).7-9 We identified a large and heterogeneous set of instruments across 382 studies. They were mostly borrowed from neurological conditions typically presenting in adulthood and other pediatric psychiatric conditions, leaving one to wonder whether they have similar accuracy and clinical relevance also in pediatric movement disorders. Here, we advocate for the development of condition-specific, developmentally appropriate, and clinically meaningful tools, and outline key priorities for achieving this goal.
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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 | 0.089 | 0.185 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.013 | 0.010 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.014 |
| Open science | 0.007 | 0.005 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".