Navigating Evidence, Challenges, and Caution in the Treatment of Stimulant Use Disorders
Bibliographic record
Abstract
Amidst the opioid epidemic, harm reduction-oriented approaches have gained traction, including interventions that focus on prescribing pharmaceutical-grade psychoactive substances, such as opioids, instead of illicit versions, intending to mitigate fatal overdose risks arising from the variability in potency and additives found in illicit drugs. Stimulants have increasingly been found in the victims of opioid overdoses, further prompting some to argue for the prescription of stimulant medications for individuals with stimulant use disorders. Yet, the evidence supporting this practice remains insufficient. In this communication, we critically examine the existing evidence, challenges, and cautions surrounding the treatment of stimulant use disorder.
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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 | 0.189 | 0.421 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.004 | 0.016 |
| Scholarly communication | 0.017 | 0.019 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.018 | 0.025 |
| Insufficient payload (model declined to judge) | 0.003 | 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".