Impact of Substance Use Disorder Pharmacotherapy on Executive Function: A Narrative Review
Bibliographic record
Abstract
Substance use disorders are chronic, relapsing, and harmful conditions characterized by executive dysfunction. While there are currently no approved pharmacotherapy options for stimulant and cannabis use disorders, there are several evidence-based options available to help reduce symptoms during detoxification and aid long-term cessation for those with tobacco, alcohol and opioid use disorders. While these medication options have shown clinical efficacy, less is known regarding their potential to enhance executive function. This narrative review aims to provide a brief overview of research that has investigated whether commonly used pharmacotherapies for these substance use disorders (nicotine, bupropion, varenicline, disulfiram, acamprosate, nalmefene, naltrexone, methadone, buprenorphine, and lofexidine) effect three core executive function components (working memory, inhibitory control and cognitive flexibility). While pharmacotherapy-induced enhancement of executive function may improve cessation outcomes in dependent populations, there are limited and inconsistent findings regarding the effects of these medications on executive function. We discuss possible reasons for the mixed findings and suggest some future avenues of work that may enhance the understanding of addiction pharmacotherapy and cognitive training interventions and lead to improved patient outcomes.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".