A case against purity: prioritizing translational polysubstance use research
Bibliographic record
Abstract
PURPOSE OF REVIEW: Preclinical (nonhuman) research on neurobehavioral underpinnings of addiction often focuses on one addictive drug studied in isolation, however, this does not reflect real-world substance use patterns of polysubstance use (PSU). Here we make a case against purity, incorporating patterns of clinically relevant PSU into preclinical models. We argue that the meaningful inclusion of people with living experience as integral collaborators in translational addiction models is critical to advance the identification of novel efficacious therapeutics to reduce the harms associated with PSU. RECENT FINDINGS: Substance use disorders are complex as clinically defined and diagnosed. Further, PSU is highly prevalent and individuals may use multiple substances within the illicit drug supply which continually evolves and is tracked via surveillance efforts (e.g., the National Drug Early Warning System). Preclinical models often model monosubstance use patterns which do not reflect real world drug use and omits expertise from people who use drugs in driving preclinical addiction science. SUMMARY: Here, we argue a case against purity in the development, design, and implementation of preclinical translational studies of addictive drugs, a need for inclusion of individuals with living experience, and highlight the need for additional research on PSU across the translational spectrum.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".