GLP-1 Receptor Agonists and Substance Use Disorder: A Systematized Literature Review
Bibliographic record
Abstract
Substance use disorder (SUD) is a significant cause of morbidity and mortality worldwide, and the incentive to discover effective and safe treatments is high given the large socioeconomic and healthcare system burden associated with SUD. SUD is a considerable challenge in medicine, partly due to the limited options for pharmacological treatment. Glucagon-like peptide-1 receptor agonists (GLP-1RAs), which have become increasingly common in recent years as the evidence of their efficacy in metabolic disorders mounts, have also demonstrated potential benefit in treatment of addiction and SUD in preclinical data. This systematized review aimed to evaluate the current clinical evidence of efficacy in GLP-1RAs in SUD. Following PRISMA guidelines, a literature search was performed using the PubMed database. A total of 14 studies met the inclusion criteria. Analysis of the studies included in this review identified significant heterogeneity in methodology, population, and reported outcomes, however the preliminary evidence presented here suggests GLP-1RAs may have therapeutic benefit and offer a promising target for further research as a potential pharmacological tool in the treatment of SUD.
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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.022 | 0.022 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".