Risk of Gambling Disorder and Impulse Control Disorder With Aripiprazole, Pramipexole, and Ropinirole
Bibliographic record
Abstract
BACKGROUND: Recently, the US Food and Drug Administration issued a warning regarding the potential risk of gambling disorder, but large epidemiologic studies are lacking. METHODS: We used a large health claims database from the United States and conducted a nested case-control study. Cases were defined as subjects newly diagnosed with gambling disorder or impulse control disorder. For each case, 10 controls were selected and matched to cases by age and follow-up time and calendar time. Adjusted rate ratios were computed with conditional logistic regression. RESULTS: There are 355 cases of gambling disorder and 3550 controls along with 4341 cases of impulse control disorder and 43,410 corresponding controls. After adjusting for confounders, users of aripiprazole demonstrated an increased risk of pathologic gambling (rate ratio [RR], 5.23; 95% confidence interval [CI], 1.78-15.38) and impulse control disorder (RR, 7.71; 95% CI, 5.81-10.34). The risk was also elevated for pramipexole or ropinirole for both gambling disorder and impulse control disorder (RR, 7.61; 95% CI, 2.75-21.07; RR, 3.28; 95% CI, 2.31-4.66, respectively). CONCLUSIONS: Our study confirms an association between aripiprazole, pramipexole, or ropinirole and impulse control disorder and gambling disorder.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".