Clinical Outcomes of the First 2 Years of Implementation of the Integrated Care Pathway for Concurrent Major Depressive Disorder and Alcohol Use Disorder
Bibliographic record
Abstract
Background Both major depressive disorder (MDD) and alcohol use disorder are highly prevalent, often comorbid and cause significant socioeconomic burden. At CAMH, we have developed and integrated care pathway (ICP) to treat these disorders and evaluated its effectiveness in comparison to treatment as usual (TAU) Methods Chart review; descriptive statistics, c2 and t-tests, linear mixed effects models, Kaplan–Meier and log-rank analyses. Results Overall, 81 patients were enrolled into ICP. Comparisons of treatment retention rates between ICP patients and matched historical controls (n = 81) showed significantly lower dropout rate in ICP cohort (18.5% vs. 69.1%, P < 0.001, Fig. 1). The ICP patients demonstrated significant reduction in depressive symptoms severity (QIDS: 14.6 vs. 10.0, P < 0.001; BDI 26.3 vs. 16.2, P < 0.001), reduction in the amount of alcohol consumed weekly from 44.6 standard drinks at baseline to 12.6 (P < 0.001) by the end of treatment, which was significantly better compared to controls (56.9 vs. 25.2, P < 0.001), P = 0.014 (Fig. 2). Conclusions The ICP is a feasible approach to treatment of concurrent AUD and MDD with significantly higher retention rates than TAU. Patients demonstrate improvements on several levels including depressive symptoms, and changes in alcohol drinking patterns. Disclosure of interest The authors have not supplied their declaration of competing interest.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".