GABAergic signaling in alcohol use disorder and withdrawal: pathological involvement and therapeutic potential
Bibliographic record
Abstract
Alcohol is one of the most widely used substances. Alcohol use accounts for 5.1% of the global disease burden, contributes substantially to societal and economic costs, and leads to approximately 3 million global deaths yearly. Alcohol use disorder (AUD) includes various drinking behavior patterns that lead to short-term or long-lasting effects on health. Ethanol, the main psychoactive molecule acting in alcoholic beverages, directly impacts the GABAergic system, contributing to GABAergic dysregulations that vary depending on the intensity and duration of alcohol consumption. A small number of interventions have been developed that target the GABAergic system, but there are promising future therapeutic avenues to explore. This review provides an overview of the impact of alcohol on the GABAergic system, the current interventions available for AUD that target the GABAergic system, and the novel interventions being explored that in the future could be included among first-line therapies for the treatment of AUD.
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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) | 0.000 | 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) | 0.004 | 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".