A Systematic Review of Therapeutic Potential of Illicit Drugs: A Narrative Overview of How Cannabinoids and Psychedelics Can be Used in Medicine
Bibliographic record
Abstract
To aid the therapeutic process, illicit chemical substances and drugs are used in drug therapy. Various cultures have used illegal substances for thousands of years in medicine and various practices. The present study is a general approach to pharmaceutical science and attempts to contextualize drug-assisted therapy through an integrative literature review. All related articles were found through the electronic databases Medical Publications (Pubmed) and Scopus. The research question was related to articles written in English within the last 20 years. The database research found 77 articles on psychedelics between Jan 2000 and Oct 2021. Twenty-two articles were selected for analysis according to the inclusion criteria. In analyzing the texts, we have been able to correlate several aspects. We conclude that psychedelic therapy has great potential and that its rebirth occurs in the context of social transformations and demands in psychotherapy that are unmissable. Database searches between Jan 2000 and Oct 2021 found 1164 articles about cannabinoids. According to the inclusion criteria, only 235 articles were selected for analysis. The United States produced the most publications, followed by Canada and Australia; evidence has steadily increased between 2000 and 2021. The contents of the publications deal with beneficial and adverse health effects, the consequences of cannabis legislation, and its association with various variables. There is a lack of research on cannabis' medicinal use regarding treatments and diseases, its standardization, routes of administration, and doses, recognizing the need for more research.
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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 | 0.008 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.020 | 0.023 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".