Adding an Internet-delivered treatment to an efficacious treatment package for opioid dependence.
Bibliographic record
Abstract
OBJECTIVE: To examine the benefit of adding an Internet-delivered behavior therapy to a buprenorphine medication program and voucher-based motivational incentives. METHOD: A block-randomized, unblinded, parallel, 12-week treatment trial was conducted with 170 opioid-dependent adult patients (mean age = 34.3 years; 54.1% male; 95.3% White). Participants received an Internet-based community reinforcement approach intervention plus contingency management (CRA+) and buprenorphine or contingency management alone (CM-alone) plus buprenorphine. The primary outcomes, measured over the course of treatment, were longest continuous abstinence, total abstinence, and days retained in treatment. RESULTS: Compared to those receiving CM-alone, CRA+ recipients exhibited, on average, 9.7 total days more of abstinence (95% confidence interval [CI = 2.3, 17.2]) and had a reduced hazard of dropping out of treatment (hazard ratio = 0.47; 95% CI [0.26, 0.85]). Prior treatment for opioid dependence significantly moderated the additional improvement of CRA+ for longest continuous days of abstinence. CONCLUSIONS: These results provide further evidence that an Internet-based CRA+ treatment is efficacious and adds clinical benefits to a contingency management/medication based program for opioid dependence.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".