Bibliometric Analysis of <i>Cannabis sativa</i> Research: Trends, Challenges, and Future Directions
Bibliographic record
Abstract
Introduction: The use of medical Cannabis remains unregulated in several countries due to the scarcity of clinical studies with high scientific evidence that establish safety and efficacy of Cannabis products. This study aimed to comprehensively analyze how knowledge has been created in this field, as well as perform a bibliographic mapping to identify knowledge gaps, and investigate key authors and journals that have significantly contributed to advancing our understanding of Cannabis . Method: The study protocol was registered in the International Prospective Register of Systematic Review (CRD42020223084). A systematic search was conducted in the Cochrane Central Register of Controlled Trials, Lilacs, Google Scholar, PubMed, SciELO, Scopus, and Web of Science databases. The bibliographic analysis and mapping were registered using the VOSviewer, Sci2tool, CiteSpace, and PoP software. Results: The systematic search identified 27,597 records, with 17,020 duplicates, resulting in a total of 10,577 articles included. The authors who published the most were Marilyn Ann Huestis ( n =108) and Sagnik Bhattacharyya ( n =71), while Elisaldo A. Carlini and Raphael Mechoulam published 8 and 22 articles, respectively. The journals Drug and Alcohol Dependence ( n =297), Psychopharmacology ( n =159) and Addictive Behaviors ( n =150) were the ones that published the most on Cannabis . The journals suggest that the articles are correlated with the adverse and toxicological effects of recreational Cannabis use; however, most articles focus on medical Cannabis . The peak of publications was in 2021 ( n =1,481). The countries that published the most were the United States ( n =9,735), while Brazil occupied the 11th position ( n =422). Most publications were carried out in “Pharmacology and Pharmacy” (11.31%), followed by “Psychiatry” (7.66%) and “Medicine” (5.80%). The areas of “Neurosciences” (1.59%), “Biochemistry,” “Genetics,” and “Molecular Biology” (0.79%) were little explored. Conclusion: This study captured the characteristics of publications about Cannabis and clinical studies in the scientific literature, yielding >10,000 articles, representing a large literature review, to date. Therefore, the most productive countries included the United States, the United Kingdom, and Canada, while the most productive authors were Marilyn Ann Huestis and Sagnik Bhattacharyya, with a peak of publications in 2021. Finally, the most chosen journals were Drug and Alcohol Dependence and Psychopharmacology .
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| Bibliometrics | 0.337 | 0.359 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.001 | 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".