Problem Gambling Associated With Aripiprazole in First-Episode Psychosis Patients
Bibliographic record
Abstract
BACKGROUND: Aripiprazole (ARI), an antipsychotic drug used to treat various mental health disorders, has recently been associated with the emergence of problem gambling (PBG). However, few cases have been reported in the schizophrenia-related psychotic disorders population, and even fewer provided sufficient details to systematically assess the causality of the association. METHODS: This article describes 6 cases with first-episode psychosis in whom PBG emerged while on ARI. Detailed information was gathered from clinical staff and patients' families to systematically assess the causal link between ARI and the emergence of PBG using the Naranjo and Liverpool Adverse Drug Reaction scales. FINDINGS: Five of these cases were previously diagnosed with a substance use disorder and/or cluster B personality traits. Five had received a more potent dopaminergic antagonist treatment before being switched to ARI. Two of them had presented PBG before being diagnosed with a psychotic disorder. The level of certainty about the causal role of ARI varied from possible to certain, and in 4 cases, the 2 scales yielded different ratings. IMPLICATIONS: Although these cases suggest that ARI may be associated with the emergence of PBG in the early course of schizophrenia-related psychotic disorders, they cannot prove the causality or the strength of this association. They provide the impetus to perform adequately powered and well-controlled prospective studies to draw more definite conclusion about the causality of this association and, in the meantime, further emphasize the need to carefully assess PBG in this population.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".