Does Aripiprazole Increase Compulsive Urges to Use Substances? Case Reports and Literature Review
Bibliographic record
Abstract
PURPOSE/BACKGROUND: Aripiprazole is commonly used to treat schizophrenia, bipolar disorder, and major depressive disorder and is preferred because of its relatively favorable side-effect profile. In 2016, the Food and Drug Administration released a warning regarding the risk of new impulse control problems with aripiprazole, including urges to gamble, binge eat, shop, and engage in sexual intercourse. These problems are rare but may cause significant harm if not recognized in time. METHODS: This report presents 2 clinical cases to hypothesize that aripiprazole may increase urges and compulsive use of substances in some patients with a history of substance use disorders. RESULTS: Both individuals had a previous history of substance use disorder before starting aripiprazole; they felt unable to stop using, as if compelled to use the substances while on aripiprazole, despite having good motivation to change. They reported a decreased urge to use substances after discontinuation of aripiprazole and were able to abstain from substances for sustained periods. IMPLICATIONS/CONCLUSIONS: These case reports suggest that aripiprazole may increase urges and compulsive substance use in patients with a history of substance use disorders. The findings emphasize the importance of a thorough preprescription assessment, education, informed consent, and regular monitoring of patients prescribed aripiprazole for increased urges or compulsions to use substances, in addition to other impulsive-compulsive behaviors. Further research is needed to confirm the association.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".