Pharmacotherapy and gambling disorder: a narrative review
Bibliographic record
Abstract
Background Gambling disorder (GD) is a psychiatric disorder classified in the DSM-5 as a non-substance-related and addictive disorder with extensive health and socioeconomic impacts. Its chronic and high-relapsing nature makes it essential to find treatment strategies that improve functioning and reduce impairment associated with it. The purpose of this narrative review is to evaluate and summarize the available evidence on the effectiveness and safety of pharmacotherapy in GD.Methods An electronic literature search of Medline, Embase, and Cochrane Central was conducted to identify systematic reviews, meta-analyses, and reviews on pharmacological interventions in patients with gambling disorder. A similar search of these databases and of Prospero, Clinicaltrials.gov, and Epistemonikos was conducted to identify clinical trials that were published since 2019.Results The initial search identified 1925 articles. After screening and duplicate removal, 18 articles were included in the review (11 studies were systematic reviews and meta-analyses, 6 were reviews, and 1 was an open-label trial). Eight pharmacological agents (naltrexone, nalmefene, paroxetine, fluvoxamine, citalopram, escitalopram, lithium, and topiramate) that were studied in randomized controlled trials and open-label trials showed small to moderate effect sizes in reducing GD symptoms in some studies during post-hoc analyses.Conclusion The overall sum of evidence in the literature on the use of pharmacotherapy in GD is conflicting and inconclusive. Some studies have shown that pharmacotherapy’s role in GD is promising, especially when the choice of the agent is guided by comorbid psychiatric disorders. However, significant limitations exist in the study designs, which need to be addressed in future research on the topic. Conducting future and more rigorous trials that address the limitations in the existing literature is necessary to establish more accurate efficacy data on the use of pharmacotherapy 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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".