Nonmotor Symptom Scales in Children With Movement Disorders
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Nonmotor symptoms (NMSs) in pediatric movement disorders, such as tics, dystonia, and cerebral palsy (CP), are important to consider but can be often overlooked, despite their substantial impact on daily functioning and quality of life. Understanding which NMSs are evaluated in these patient groups, and by which tools, can support the shaping of future research, including the potential development of future dedicated NMS assesment instruments. The aim of this scoping review was to identify the scales and tools used in routine practice and/or in research to assess NMSs in children with movement disorders. METHODS: A comprehensive search of MEDLINE, Embase, and PsycINFO was conducted for studies published between 1990 and 2023. Eligible studies included those that evaluated NMSs using scales in children aged 0-18 years with tic disorders, dystonia, or CP. RESULTS: A total of 382 studies were included. Most of the articles identified were cross-sectional, cohort, and case-control studies. Cognitive impairment, mental health, behavioral difficulties, and pain were most frequently assessed using standardized scales. However, self-esteem and communication-critical components of social functioning-were rarely evaluated. The assessment of sleep disturbance, fatigue, and gastrointestinal and urinary symptoms was also less frequently addressed. Notably, our methodological approach may have led to an overrepresentation of NMS assessment in CP, given the larger body of literature available for this condition. DISCUSSION: Significant gaps exist in the evaluation of NMSs in pediatric movement disorders, particularly in areas such as pain, sleep, and gastrointestinal issues. While standardization of NMS assessment is needed, it is unclear whether disorder-specific tools are preferable to broader NMS-focused measures. Given the current lack of data, using general scales may be a pragmatic first step, with refinement into disorder-specific tools as our understanding of symptom patterns evolves. Cross-cultural validation is also essential to improve the applicability of NMS scales across diverse populations. Integrating NMS assessment into routine clinical practice and interdisciplinary care may facilitate early identification and better management of these symptoms.
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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 | 0.009 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".