Predicting Change in an Integrated Dual Diagnosis Substance Abuse Intensive Outpatient Program
Bibliographic record
Abstract
Research on routine outcome monitoring in psychotherapy settings is plentiful but not without implementation obstacles. In fact, there is a relative dearth of real-time outcome monitoring in substance use treatment settings. Numerous barriers to the development and implementation of clinical decision support tools and outcome monitoring of substance use patients, including the need to establish expected trajectories of change and use of reliable change indices have been identified (Goodman, McKay, & DePhilippis, 2013 ). The current study was undertaken to develop expected trajectories of change and to demonstrate the treatment effectiveness of a dual diagnosis intensive outpatient program. The expected trajectories of change for days of substance use and depression scores were developed using predictive equation models from derivation samples and then applied to cross-validation samples. Predictive equations to monitor substance use were developed and validated for all patients and for only patients who were actively using substance at the time of admission, as well as to monitor severity of their depression symptom on a weekly basis. Validation of the equations was assessed through the use of Cohen's kappa (κ), receiver operating characteristic curves, reliable change index, and percentage improvement. Large effect sizes for reductions in substance use (Cohen's d = .76) and depressive symptoms (d = 1.10) are reported. The best predictive models we developed had absolute accuracy rates ranging from 95 to 100%. The findings from this study indicate that predictive equations for depressive symptoms and days of substance use can be derived and validated on dual diagnosis samples.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".