Adverse effects of antipsychotic medication in patients with 22q11.2 deletion syndrome: A systematic review
Bibliographic record
Abstract
The 22q11.2 deletion syndrome (22q11.2DS) is a multisystem condition and the most prevalent microdeletion syndrome in humans. Approximately 25% of individuals with 22q11.2DS receive antipsychotic treatment. To assess whether patients with 22q11.2DS are vulnerable to adverse effects of antipsychotic medication, we carried out a literature review. A systematic search strategy was performed using PubMed (Medline), Embase, PsychInfo, and Cochrane Database of Systematic Reviews. Publications describing adverse effects of antipsychotic medication in patients with 22q11.2DS were included in the review and assessed for their methodological quality. A total of 11 publications reporting on eight trials, cross-sectional or cohort studies, and 30 case reports were included. The most commonly reported adverse effects can be classified into the following categories: movement disorders, weight gain, seizures, cardiac side effects, and cytopenias. Many of these symptoms are manifestations of 22q11.2DS, also in the absence of antipsychotic medication. Based on the reviewed literature, a causal relation between antipsychotic medication and the reported adverse effects could not be established in the majority of cases. Randomized clinical trials are needed to make firm conclusions regarding risk of adverse effects of antipsychotics in patients with 22q11.2DS.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".