Pharmacological Treatments for Disordered Gambling: A Meta-analysis
Bibliographic record
Abstract
Disordered gambling is a public health concern associated with detrimental consequences for affected individuals and social costs. Currently, opioid antagonists are considered the first-line treatments to reduce symptoms of uncontrolled gambling. Only recently, glutamatergic agents and combined pharmacological and psychological treatments have been examined appearing promising options for the management of gambling disorder. A multilevel literature search yielded 34 studies including open-label and placebo-controlled trials totaling 1340 participants to provide a comprehensive evaluation of the short- and long-term efficacies of pharmacological and combined treatments. Pharmacological treatments were associated with large and medium pre-post reductions in global severity, frequency, and financial loss (Hedges's g: 1.35, 1.22, 0.80, respectively). The controlled effect sizes for the outcome variables were significantly smaller (Hedges's g: 0.41, 0.11, 0.22), but robust for the reduction of global severity at short-term. In general, medication classes yielded comparable effect sizes independent of predictors of treatment outcome. Of the placebo controlled studies, results showed that opioid antagonists and mood stabilizers, particularly the glutamatergic agent topiramate combined with a cognitive intervention and lithium for gamblers with bipolar disorders demonstrated promising results. However, more rigorously designed, large-scale randomized controlled trials with extended placebo lead-in periods are necessary. Moreover, future studies need to monitor concurrent psychosocial treatments, the type of comorbidity, use equivalent measurement tools, include outcome variables according to the Banff, Alberta Consensus, and provide follow-up data in order to broaden the knowledge about the efficacy of pharmacological treatments for this disabling condition.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.042 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".