Seeking order in patterns of polysubstance use
Bibliographic record
Abstract
PURPOSE OF REVIEW: This review provides an overview of recent developments in understanding polysubstance use patterns across the lifespan, and advances made in the prevention and treatment of harm arising from polysubstance use. RECENT FINDINGS: A comprehensive understanding of the patterns of polysubstance use is hampered by heterogeneity across study methods and types of drugs measured. Use of statistical techniques such as latent class analysis has aided in overcoming this limitation, identifying common patterns or classes of polysubstance use. These typically include, with decreasing prevalence, (1) Alcohol use only; (2) Alcohol and Tobacco; (3) Alcohol, Tobacco, and Cannabis; and finally (4) a low prevalence, Extended Range cluster that includes other illicit drugs, New Psychoactive Substances (NPS), and nonmedical prescription medications. SUMMARY: Across studies, there are commonalities present in clusters of substances used. Future work that integrates novel measures of polysubstance use and leverages advances in drug monitoring, statistical analysis and neuroimaging will improve our understanding of how and why drugs are combined, and more rapidly identify emerging trends in multiple substance use. Polysubstance use is prevalent but there is a paucity of research exploring effective treatments and interventions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| 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) | 0.004 | 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".