A framework to assess risks for non-medical use of psychedelics: The liability for abuse of psychedelics questionnaire (LAPQ)
Bibliographic record
Abstract
: As psychedelic research moves forward, trial design could benefit from assessments of potential risks of treatment. One such risk is future abuse or misuse of the drug or other drugs of abuse. Like the ketamine literature, psychedelic studies to date have not included measures designed to thoroughly address this risk. With aims to fill this gap, we previously developed a ketamine/esketamine drug liking and craving questionnaire (DLCQ), which primarily considered level of drug liking as a risk factor for potential future misuse or abuse. In adapting this for use with psychedelics, several considerations arose, including the likelihood that the psychedelic experience may be more universally pleasurable, and that desire to use psychedelics again may carry several underlying reasons. Here, we describe the Liability for Abuse of Psychedelics Questionnaire (LAPQ), which provides a framework incorporating 3 domains; substance use history, liking and craving for the drug in question, and subsequent changes in substance use patterns after exposure to the drug. While not a validated instrument, we propose this framework may be used in conjunction with other side effect tracking tools to more comprehensively address risks in psychedelic studies.
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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 | 0.034 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.007 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".