Predictors of non-use of illicit heroin in opioid injection maintenance treatment of long-term heroin dependence
Bibliographic record
Abstract
AIMS: To investigate baseline and concurrent predictors of non-use of illicit heroin among participants randomized to injectable opioids in the North American Opiate Medication Initiative (NAOMI) clinical trial. METHODS: NAOMI was an open-label randomized controlled trial comparing the effectiveness of injectable diacetylmorphine and hydromorphone for long-term opioid-dependency. Outcomes were assessed at baseline and during treatment (3, 6, 9, 12months). Days of non-use of illicit heroin in the prior month at each follow-up visit were divided into three categories: Non-use; Low use (1 to 7days) and High use (8days or more). Tested covariates were: Sociodemographics, Health, Treatment, Drug use and illegal activities. Mixed-effect proportional odds models with random intercept for longitudinal ordinal outcomes were used to assess the predictors of the non-use of illicit heroin. RESULTS: 139 participants were included in the present analysis. At each follow-up visit, those with non-use of illicit heroin represented 47.5% to 54.0% of the sample. Fewer days of cocaine use (p=0.074), fewer days engaged in illegal activities at baseline (p<0.01) and at each visit (p<0.01), less money spent on drugs (p<0.001), days with injection opioid or oral methadone treatment (p<0.001) and total mg of injectable opioids taken (p<0.001), independently predicted lower use of illicit heroin. CONCLUSIONS: The independent effect of several concurrent factors besides the injection of opioid dose suggests benefits from the clinic that go beyond the provision of the medication alone. Thus, this supervised model of care presents an opportunity to maximize the beneficial impact of medical and psychosocial components of the treatment on improving outcomes associated with non-use of illicit heroin.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".