Marijuana Neurobiology and Treatment
Bibliographic record
Abstract
Marijuana is the number one illicit drug of abuse worldwide and a major public health problem, especially in the younger population. The objective of this article is to update and review the state of the science and treatments available for marijuana dependence based on a pre-meeting workshop that was presented at ISAM 2006. At the workshop, several papers were presented addressing the neurobiology and pharmacology of marijuana and treatment approaches, both psychotherapy and medications, for marijuana withdrawal. Medicolegal and ethical issues concerning marijuana medical use were also discussed. Concise summaries of these presentations are incorporated in this article, which is meant to be an updated review of the state of the science. Major advances have been made in understanding the underpinning of marijuana dependence and the role of the CNS cannabinoid system, which is a major area for targeting medications to treat marijuana withdrawal and dependence, as well as other addictions. Behavioral therapies are efficacious for facilitating abstinence from marijuana. Nefazadone, Marinol, and buspirone are showing early positive signals for efficacy in ameliorating marijuana withdrawal symptoms. Effective psychotherapeutic approaches are available and promising medications studies need to be confirmed in outpatient trials. The next few years looking promising for translational research efforts to make treatment widely accessible to patients with marijuana dependence.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".