Powder and Crack Cocaine Use Among Opioid Users
Bibliographic record
Abstract
OBJECTIVES: Problematic cocaine use is highly prevalent and is a significant public health concern. However, few investigations have distinguished between the 2 formulations of cocaine (ie, powder and crack cocaine) when examining the characteristics of cocaine use. Moreover, research has yet to assess the patterns of powder and crack cocaine use among opioid users, a clinical population in which problematic cocaine use is increasingly common. Using a within-subjects design, this study examined whether opioid users reported different patterns and features of powder and crack cocaine use, along with distinct trajectories and consequences of use. METHODS: Seventy-three clients enrolled in a low-threshold methadone maintenance treatment were interviewed regarding their lifetime use of powder and crack cocaine. RESULTS: Compared with crack cocaine, initiation and peak use of powder cocaine occurred at a significantly younger age. In relation to recent cocaine use, participants were significantly more likely to report using crack cocaine than using powder cocaine. Differences in routes of administration, polysubstance use, and criminal activity associated with cocaine use were also found between the 2 forms of cocaine. CONCLUSIONS: Results suggest that it may not be appropriate to consider powder and crack cocaine as diagnostically and clinically equivalent. As such, researchers may wish to distinguish explicitly between powder and crack cocaine when assessing the characteristics and patterns of cocaine use among substance users and treat these 2 forms of cocaine separately in analyses.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".