Association of prescribed oral stimulants on cocaine use among patients enrolled in opioid agonist treatment: A retrospective longitudinal cohort study
Bibliographic record
Abstract
Objectives The objective of this study was to measure the association of prescribed oral stimulants with the consumption of cocaine among a population of patients receiving Opioid Agonist Therapy (OAT). Methods The study was a retrospective clinical cohort study using the medical records of all patients receiving OAT who attended treatment clinics within the Canadian Addiction Treatment Centers (CATC) in Ontario from April 2014 to February 2021. Linear mixed-effects models were fit for the exposure of prescribed oral stimulants, and the outcome of a positive urinalysis drug screen for cocaine. Covariates for age, sex, and a random effect for patients were fitted to account for differences between and within patient observations over time. Results Among patients receiving OAT therapyn= 314 patients were prescribed oral stimulants andn= 11,879 patients were not prescribed oral stimulants among Ontario CATC clinics (n= 92,n= 145 physicians), the mean age at enrollment for patients receiving oral stimulants was 37.0,SD= 8.8, with 43.6% female patients and for patients not receiving oral stimulants mean age was 36.6,SD= 10.7, with 39.6% female patients. Linear mixed effects models showed no difference in cocaine-positive urine tests over time for fixed effectsB= 0.001, however, when considering the Interclass correlation coefficient (ICC) between the fixed effects, we found that time since the prescription of an oral stimulant was associated with a decrease of ICC = −0.14 in cocaine positive urine tests. Increasing age at prescription ICC = −0.92, and being male ICC = −0.23 were associated with decreasing cocaine-positive urine. Conclusion The use of oral stimulant prescriptions to treat cocaine use had no clinically significant benefit in a real-world setting. Patients who receive prescriptions for oral stimulants consume more cocaine before and after treatment compared to patients without an oral stimulant prescription. We also observed that cocaine use was reduced with increased time since treatment initiation.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".