Helping Opioid Use Disorder and PTSD with Exposure (HOPE): An Open-Label Pilot Study of a Trauma-Focused, Integrated Therapy for OUD/PTSD
Bibliographic record
Abstract
Opioid use disorder (OUD) and posttraumatic stress disorder (PTSD) frequently co-occur. However, there are no psychotherapy treatments intentionally designed for this comorbidity, nor designed to be augmented with medications for OUD. In this open-label pilot trial, we tested Helping Opioid Use Disorder and PTSD with Exposure (HOPE), a novel integrated, trauma-focused treatment for individuals (N = 6) with OUD/PTSD who were stabilized on medications for OUD. HOPE was delivered weekly for 10–12 sessions, and one follow-up visit was conducted ~1-month post-treatment. Primary outcomes included urine drug screens, the Timeline Followback, Desire for Drugs Questionnaire, Clinician-Administered PTSD Scale-5 (CAPS-5), and PTSD Checklist-5 (PCL-5). Boot-strapped linear mixed effect models and generalized estimating equations showed that PTSD symptoms (CAPS-5: B = −7.16, SE = 1.24, p < 0.01; PCL-5: B = −2.04, SE = 0.26, p < 0.01), desire for opioids (B = −0.56, SE = 0.15, p < 0.01), depression symptoms (B = −0.43, SE = 0.09, p < 0.01), and anxiety symptoms (B = −0.50, SE = 0.08, p < 0.01) decreased significantly over time. Client satisfaction increased throughout the study (B = 0.18, SE = 0.08, p = 0.02), and 83.3% of participants completed the therapy and follow-up visit. There were no significant changes in opioid or other substance use from baseline to follow-up. Although preliminary, results show high acceptability and feasibility of the HOPE therapy and demonstrate significant improvements in PTSD and associated symptoms with an integrated, trauma-focused treatment.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".