Emerging therapies for childhood-onset movement disorders
Bibliographic record
Abstract
PURPOSE OF REVIEW: We highlight novel and emerging therapies in the treatment of childhood-onset movement disorders. We structured this review by therapeutic entity (small molecule drugs, RNA-targeted therapeutics, gene replacement therapy, and neuromodulation), recognizing that there are two main approaches to treatment: symptomatic (based on phenomenology) and molecular mechanism-based therapy or 'precision medicine' (which is disease-modifying). RECENT FINDINGS: We highlight reports of new small molecule drugs for Tourette syndrome, Friedreich's ataxia and Rett syndrome. We also discuss developments in gene therapy for aromatic l-amino acid decarboxylase deficiency and hereditary spastic paraplegia, as well as current work exploring optimization of deep brain stimulation and lesioning with focused ultrasound. SUMMARY: Childhood-onset movement disorders have traditionally been treated symptomatically based on phenomenology, but focus has recently shifted toward targeted molecular mechanism-based therapeutics. The development of precision therapies is driven by increasing capabilities for genetic testing and a better delineation of the underlying disease mechanisms. We highlight novel and exciting approaches to the treatment of genetic childhood-onset movement disorders while also discussing general challenges in therapy development for rare diseases. We provide a framework for molecular mechanism-based treatment approaches, a summary of specific treatments for various movement disorders, and a clinical trial readiness framework.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".