Dissecting the Phenotypic Spectrum and Complexity of Movement Disorders in 22q11.2 Deletion Syndrome
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Movement disorders are increasingly recognized as late-occurring neurologic manifestations of 22q11.2 deletion syndrome (22q11.2DS). We aimed to dissect the spectrum of relevant movement disorders in 22q11.2DS, including clinical and electrophysiologic presentations and effective therapies. METHODS: Retrospective review of medical records, medication histories, and videotaped examinations was conducted in 31 unrelated adults (55% female) diagnosed with 22q11.2DS and a movement disorder who were seen at a major center of excellence from June 1996 to September 2023. Between-group comparisons were performed to explore the influence of medications on movement disorder presentations. RESULTS: The median age at movement disorder onset was 35.5 (IQR: 22.0) years. Non-parkinsonian tremor was the most common phenotype (21/31, 68%), followed by parkinsonism (13/31, 42%), dystonia (11/31, 36%), myoclonus (9/31, 29%), dyskinesia (6/31, 19%), stereotypies, and functional movement disorders (4/31, 13% each). The majority of patients (24/31, 77%) presented with two or more movement disorder phenotypes (median 3, range: 2-7). Similar trends in prevalence emerged after accounting for antipsychotic exposure and potential drug-related movement disorders. Electrophysiological assessments identified both previously described and novel motor phenotypes. Treatment data for at least one movement disorder (available for 20/31, 65%) indicated a positive response to standard phenotype-based interventions. CONCLUSIONS: We demonstrate that movement disorders in adults with 22q11.2DS exhibit greater clinical complexity than previously reported, which could reflect innate vulnerability and pathologic mechanisms beyond medication side effects. In those with a confirmed 22q11.2 microdeletion, periodic neurologic evaluations, supported by electrophysiologic investigations, enable accurate diagnosis and implementation of personalized management strategies.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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) | 0.001 | 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".