Cannabis self-administration in the human laboratory: a scoping review of ad libitum studies
Bibliographic record
Abstract
Cannabis self-administration studies may be helpful for identifying factors that influence cannabis consumption and subjective response to cannabis. Additionally, these paradigms could be useful for testing novel pharmacotherapies for cannabis use disorder. This scoping review aims to summarize the findings from existing ad libitum cannabis self-administration studies to determine what has been learned from these studies as well as their limitations. We examined studies that specifically examined cannabis smoking, focusing on subjective response and self-administration behavior (e.g., smoking topography). A systematic search was conducted using PubMed and Embase from inception to October 22, 2022. Our search strategy identified 26 studies (total N = 662, 79% male) that met our eligibility criteria. We found that tetrahydrocannabinol (THC) concentration significantly affected subjective response to cannabis in some but not all studies. In general, cannabis self-administration tended to be most intense at the beginning of the laboratory session and decreased in later parts of the session. There was limited data on cannabis self-administration in adults older than 55. Data on external validity and test-retest reliability were also limited. Addressing these limitations in future ad libitum cannabis self-administration studies could lead to more valid and generalizable paradigms, which in turn could be used to improve our understanding of cannabis use patterns and to help guide medication development for cannabis 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 | 0.010 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.014 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".