Integrated Treatment of Comorbid Depression and Substance Use Disorders
Bibliographic record
Abstract
OBJECTIVE: The goals of this 6-month prospective study were to evaluate the effect of a current diagnosis of depression on the course and outcome of addiction treatment and to determine whether patients with depression received or required additional treatment compared with those without depression. METHOD: On entering addiction treatment, 75 men and 45 women with substance use disorders were assessed by clinical and semistructured interviews, Global Assessment Scale, Hamilton Rating Scale for Depression, Beck Depression Inventory, and revised 90-item Symptom Checklist. RESULTS: Forty-three patients (35.8%) met DSM-IV criteria for a current depressive disorder at intake into addiction treatment. The depressed patients had significantly (p < .0001) higher levels of psychopathology at intake. However, contrary to previous studies, they fared as well as the nondepressed patients in terms of all addiction outcome measures and all indicators of psychiatric status at 6 months. During the 6-month follow-up period, the depressed patients received more treatment than the nondepressed patients. Specifically, they had more psychiatric appointments, and they were more likely to require inpatient detoxification and to be prescribed new antidepressant medication regimens. CONCLUSION: Depression comorbidity may not have had a negative impact on the course and outcome of addiction treatment because the dual disorder was identified at the initial assessment, and integrated psychiatric care was available. It may be that additional treatment compensated for greater psychopathology among dual-disorder patients.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".