Neuropsychiatric expression and catatonia in 22q11.2 deletion syndrome: An overview and case series
Bibliographic record
Abstract
Individuals with 22q11.2 deletion syndrome (22q11.2DS) are at elevated risk of developing treatable psychiatric and neurological disorders, including anxiety disorders, schizophrenia, seizures, and movement disorders, often beginning in adolescence or early to mid-adulthood. Here, we provide an overview of neuropsychiatric features associated with 22q11.2DS in adulthood. Results of a new case series of 13 individuals with 22q11.2DS and catatonic features together with 5 previously reported cases support a potential association of this serious psychomotor phenotype with the 22q11.2 deletion. As in the general population, catatonic features in 22q11.2DS occurred in individuals with schizophrenia, other psychotic and non-psychotic psychiatric disorders, and neurological disorders like Parkinson's disease. We place the results in the context of an updated review of catatonia in other genetic conditions. The complex neuropsychiatric expression and risk profile of 22q11.2DS highlights the need to consider co-morbid factors and provide care tailored to the individual patient. The results reinforce the need for periodic monitoring for the emergence of psychiatric and neurological manifestations including catatonic features. Pending further research, enhanced recognition and informed anticipatory care promise to facilitate the early diagnosis that allows for timely implementation and optimization of effective treatments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".