Prescription psychostimulants for cocaine use disorder: A review from molecular basis to clinical approach
Bibliographic record
Abstract
Cocaine use is a public health concern in many countries worldwide, particularly in the Americas and Oceania. Overdose deaths involving stimulants, such as cocaine, have been increasing markedly in North America, especially with concurrent opioid involvement. To date, no pharmacological treatment is available to treat stimulant (including cocaine) use disorders. Prescription psychostimulants (PPs) could be useful to treat cocaine use disorder (CUD) as they share the pharmacological effects with cocaine, as evidenced by a recent meta-analysis that assessed 38 randomized clinical trials (RCTs). PPs were found to promote sustained abstinence and reduce drug use in patients with CUD. The aim of this paper is to provide a narrative review of the clinical pharmacology of PPs and comment on the current stage of evidence supporting PPs to treat CUD. We also propose a model of care that integrates PPs with evidence-based psychosocial interventions (such as cognitive-behavioural therapy [CBT] and contingency management [CM]), a harm reduction approach and case management with social support.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".