Theory and Practice of Treatment of Concurrent Major Depressive and Alcohol Use Disorders
Bibliographic record
Abstract
Introduction Both Major Depressive and Alcohol Use Disorders are highly prevalent. They also are the major contributors to disability and decreased quality of life and, as they are often comorbid with each other, the diagnosis and treatment of concurrent depression and alcohol use disorder represents a challenging task with multiple clinical questions requiring evidence-based recommendations. Objectives The goal of this presentation is to review the optimal strategies to treat concurrent alcohol use and major depressive disorders in the context of current research findings and clinical practice. Methods Narrative review, knowledge synthesis. Results The most up-to-date research findings in the areas of epidemiology of concurrent depression and alcohol use disorder, their differential diagnosis, and treatment approaches will be reviewed. This review will include the current evidence of effectiveness of various antidepressants in treatment of depression concurrent with alcohol use disorder and antidipsotropic agents use for alcohol use disorder in the context of depressive symptoms, as well as their combinations. We will discuss the timeline of initiation of both antidepressants and antidipsotropic agents, non-pharmacological treatment modalities as well as the clinical tools that can be used to properly monitor patients’ progress and optimize the treatment process, and the integrative teamwork necessary to achieve optimal results. Conclusions Ultimately, the optimal diagnostic and treatment algorithm and the set of evidence-based treatment recommendations will be presented. Disclosure No significant relationships.
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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 | 0.014 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| 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".