Case Reports of Aripiprazole and Problematic Gambling in Schizophrenia
Bibliographic record
Abstract
BACKGROUND: Pharmacovigilance studies have reported a higher risk of problematic gambling (PG) in people receiving aripiprazole (ARI), a partial dopamine agonist. This association needs to be specifically assessed in schizophrenia (SZ) given the high prevalence of risk factors for PG in this population (eg, comorbid substance use) and given the nature of the dopamine dysfunction in this disorder. At the present stage, case studies may shed light on such an association. METHODS: All published cases involving SZ patients with PG while on ARI were systematically identified. Two instruments were used to assess causality. RESULTS: We identified 16 published SZ cases exposed to ARI experiencing PG. Half of whom had a gambling history before ARI exposition. Naranjo scores led to the estimation of a possible link between ARI exposition and PG in 15 of 16 cases (average score of 3) and probable (score of 5) in 1 case. More than 50% of items were left unknown owing to the lack of information or scale limitations. Using the Liverpool algorithm, causality estimation was raised to probable in 13 of 16 cases, definite in 1 case, and nonassessable in 2 cases. CONCLUSIONS: The present review confirms that ARI may be involved in the occurrence of PG in some SZ patients. However, important information to assess causality was frequently missing, and the 2 scales used did not yield the same degree of certainty. The current article calls for including more details in future case reports and for well-powered studies carefully assessing factors such as comorbid diagnoses.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".