Alcohol Use Disorder: Neurobiology and Therapeutics
Bibliographic record
Abstract
Alcohol use disorder (AUD) encompasses the dysregulation of multiple brain circuits involved in executive function leading to excessive consumption of alcohol, despite negative health and social consequences and feelings of withdrawal when access to alcohol is prevented. Ethanol exerts its toxicity through changes to multiple neurotransmitter systems, including serotonin, dopamine, gamma-aminobutyric acid, glutamate, acetylcholine, and opioid systems. These neurotransmitter imbalances result in dysregulation of brain circuits responsible for reward, motivation, decision making, affect, and the stress response. Despite serious health and psychosocial consequences, this disorder still remains one of the leading causes of death globally. Treatment options include both psychological and pharmacological interventions, which are aimed at reducing alcohol consumption and/or promoting abstinence while also addressing dysfunctional behaviours and impaired functioning. However, stigma and social barriers to accessing care continue to impact many individuals. AUD treatment should focus not only on restoring the physiological and neurological impairment directly caused by alcohol toxicity but also on addressing psychosocial factors associated with AUD that often prevent access to treatment. This review summarizes the impact of alcohol toxicity on brain neurocircuitry in the context of AUD and discusses pharmacological and non-pharmacological therapies currently available to treat this addiction disorder.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.006 | 0.002 |
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".