Outcomes of an integrated care pathway for concurrent major depressive and alcohol use disorders: a multisite prospective cohort study
Bibliographic record
Abstract
In 2013, an Integrated Care Pathway (ICP) for concurrent Major Depressive (MDD) and Alcohol Use (AUD) Disorders was developed at the Centre for Addiction and Mental Health (CAMH), Toronto, Ontario, Canada. The ICP was further implemented at 8 other clinical sites across Ontario (the DA VINCI Project) in 2015–2017. The goal of this study was to systematically describe and analyze the main clinical outcomes of the project. Data on a non-randomized cohort of patients receiving ICP-based treatment were collected prospectively at nine clinical sites in a variety of clinical settings. Statistical methods: descriptive statistics, t-test, chi-square, ANOVA, generalized linear models. Two hundred forty-six patients were enrolled, 58.8% males, mean age was 45.6 years, 170 patients received treatment at academic health centres (AHC), 49 – at community hospitals (CH) and 27 – in family health teams (FHT). There were no major differences in anamnestic parameters and depression severity between the three settings, but there were differences in baseline drinking patterns between subgroups (F = 4.271, df = 2, p = 0.015). Overall completion rate was 70.7% with no significant variation between settings (χ 2 = 3.35, df = 2, p = 0.19). Treatment duration in AHC was the longest, and completion rates were the highest. There was a statistically significant and clinically meaningful reduction in the number of drinking days per week (1.81, t = 8.78, p < 0.001). The cohort overall demonstrated significant and meaningful reduction in severity of cravings (Penn Alcohol Craving Scale: 4.42, t = 8.63, p < 0.001) and depressive symptoms (Quick Inventory of Depressive Symptomatology: 4.25, t = 11.26, p < 0.001). While some of the baseline patient characteristics and treatment parameters varied between the settings, the variation in clinical outcomes was mostly insignificant, though clinical improvement was more pronounced in academic setting and with individual therapy. The study demonstrated that ICP is a feasible and effective treatment for concurrent AUD and MDD that delivers meaningful clinical improvement in a variety of settings. A randomized controlled study is needed to properly compare the treatment outcomes between ICP model and treatment as usual and to further explore the role of various factors on treatment outcomes.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".