Theory and Practice of Treatment of Concurrent Major Depressive and Alcohol Use Disorders: 7 Lessons from Clinical Practice and Research
Bibliographic record
Abstract
ABSTRACT Objectives: Both major depression and alcohol use are highly prevalent in the Canadian population. They 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 represent a challenging task with multiple clinical questions requiring evidence-based recommendations. Thus, the goal of this article 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, and secondary data analysis. Results: Based on the review of the relevant literature and secondary data analyses of our own clinical data, we devised a set of pragmatic clinical recommendations and guidance on differential diagnosis between alcohol-induced mood disorder and independent major depressive disorder concurrent with alcohol use disorder, the choice and timing of pharmacological agents, organization of care, selection of best-evidence psychotherapeutic approaches and their integration into clinical practice, management of patients’ and team expectations in terms of clinical outcomes, as well as the implementation of measurement-based approaches to optimize care delivery and achieve better clinical outcomes. Conclusions: Seven clinically relevant problems were reviewed and the evidence-based ready-to-implement clinical approaches were offered. Objectifs: La dépression majeure et la consommation d’alcool sont très répandues dans la population canadienne. Ils sont les principaux contributeurs à l’invalidité et à la diminution de la qualité de vie et, comme ils sont souvent comorbides les uns avec les autres, le diagnostic et le traitement de la dépression concomitante et des troubles liés à la consommation d’alcool représentent une tâche difficile avec de multiples questions cliniques nécessitant des recommandations fondées sur des preuves. Ainsi, le but de cet article est d’examiner les stratégies optimales pour traiter la consommation concomitante d’alcool et les troubles dépressifs majeurs dans le contexte des résultats de recherche actuels et de la pratique clinique. Méthodes: Revue narrative, synthèse des connaissances, analyse des données secondaires. Résultats: Sur la base de la revue de la littérature pertinente et des analyses de données secondaires de nos propres données cliniques, nous avons conçu un ensemble de recommandations cliniques pragmatiques et de conseils sur le diagnostic différentiel entre les troubles de l’humeur induits par l’alcool et les troubles dépressifs majeurs indépendants concomitants avec les troubles liés à la consommation d’alcool, le choix et le timing des agents pharmacologiques, l’organisation des soins, la sélection des approches psychothérapeutiques les plus probantes et leur intégration dans la pratique clinique, la gestion des attentes des patients et des équipes en terme de résultats cliniques ainsi que la mise en œuvre d’approches basées sur la mesure afin d’optimiser la prestation des soins et obtenir de meilleurs résultats cliniques. Conclusions: Sept problèmes cliniquement pertinents ont été examinés et des approches cliniques fondées sur des preuves prêtes à être mises en œuvre ont été proposées.
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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 | 0.131 | 0.139 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.005 | 0.034 |
| Scholarly communication | 0.016 | 0.012 |
| Open science | 0.010 | 0.009 |
| Research integrity | 0.011 | 0.016 |
| Insufficient payload (model declined to judge) | 0.002 | 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".