Movement Disorders Associated with 22q11.2 Microdeletion: A Scoping Review
Bibliographic record
Abstract
BACKGROUND: Movement disorders have recently emerged as important neurologic manifestations of the 22q11.2 microdeletion that affects nearly one in every 2000 live births. OBJECTIVE: We aimed to map the existing evidence regarding the spectrum, diagnosis and treatment, and etiopathogenesis of movement disorders associated with 22q11.2 microdeletion, highlight key gaps, and provide recommendations for future research. METHODS: We conducted a comprehensive search of MEDLINE, Embase, CENTRAL, and reference lists to identify relevant clinical and preclinical studies, and summarize data on clinico-pathologic features, risk factors, and pathophysiology of movement disorders associated with molecularly defined 22q11.2 microdeletions. RESULTS: Forty-three (clinical: n = 41/43, 95%; preclinical: n = 2/43, 5%) studies met eligibility criteria. Most clinical studies (35/41, 85%) involved adults, of which 51% (21/41) were case series/reports, and the remainder observational studies. Key findings included: (1) emphasis on parkinsonism but emerging evidence for multiple other motor phenotypes, (2) non-specific findings on routine neuroimaging, albeit with promising early results of certain modalities (eg, volumetric analyses, molecular imaging) and fluid-based biomarkers, particularly for parkinsonism, and (3) multifaceted etiologic and pathophysiologic processes beyond those attributable to medication side effects. Prevailing gaps include research design limitations, heterogeneity of diagnostic methods, and limited systematic data on etiopathogenesis and treatments. CONCLUSIONS: Current evidence supports an expanding spectrum of motor phenotypes associated with 22q11.2 microdeletion, with emerging data reporting potentially useful clinical markers and non-drug-related risk modifiers and underlying mechanisms. Addressing prevailing gaps will require enhanced research designs with up-to-date diagnostic and genetic methodologies, robust preclinical models, and cross-disciplinary collaborations.
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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 | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.018 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| 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".