Systematic review on intentional non-medical fentanyl use among people who use drugs
Bibliographic record
Abstract
Objectives: Fentanyl is a highly potent opioid and has, until recently, been considered an unwanted contaminant in the street drug supply among people who use drugs (PWUD). However, it has become a drug of choice for an increasing number of individuals. This systematic review evaluated intentional non-medical fentanyl use among PWUD, specifically by summarizing demographic variance, reasons for use, and resulting patterns of use. Methods: The search strategy was developed with a combination of free text keywords and MeSH and non-MeSH keywords, and adapted with database-specific filters to Ovid MEDLINE, Embase, Web of Science, and PsychINFO. Studies included were human studies with intentional use of non-medical fentanyl or analogues in individuals older than 13. Only peer-reviewed original articles available in English were included. Results: The search resulted in 4437 studies after de-duplication, of which 132 were selected for full-text review. Out of 41 papers included, it was found that individuals who use fentanyl intentionally were more likely to be young, male, and White. They were also more likely to have experienced overdoses, and report injection drug use. There is evidence that fentanyl seeking behaviours are motivated by greater potency, delay of withdrawal, lower cost, and greater availability. Conclusions: Among PWUD, individuals who intentionally use fentanyl have severe substance use patterns, precarious living situations, and extensive overdose history. In response to the increasing number of individuals who use fentanyl, alternative treatment approaches need to be developed for more effective management of withdrawal and opioid use disorder. Systematic review registration: https://www.crd.york.ac.uk/prospero/, identifier CRD42021272111.
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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 | 0.007 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".