A bibliometrics and visualization analysis of cannabidiol research from 2004 to 2021
Bibliographic record
Abstract
Cannabidiol, a non-psychoactive component extracted from the plant cannabis sativa, has gained growing focus in recent years since its extensive pharmacology effects have been founded. The purpose of this study intends to reveal the hot spots and frontiers of cannabidiol research using bibliometrics and data visualization methods. A total of 3,555 publications with 106,793 citations from 2004 to 2021 related to cannabidiol were retrieved in the Web of Science database, and the co-authorships, research categories, keyword burst, and reference citations in the cannabidiol field were analyzed and visualized by VOSviewer and Citespace software. Great importance has been attached to the pharmacology or pharmacy values of cannabidiol, especially in the treatment of neuropsychiatric disorders, such as epilepsy, anxiety, and schizophrenia. The mechanisms or targets of the cannabidiol have attracted the extreme interest of the researchers, a variety of receptors including cannabinoids type 1, cannabinoids type 2, 5-hydroxytriptamine1A, and G protein-coupled receptor 55 were involved in the pharmacology effects of cannabidiol. Moreover, the latest developed topic has focused on the positive effects of cannabidiol on substance use disorders. In conclusion, this study reveals the development and transformation of knowledge structures and research hotspots in the cannabidiol field from a bibliometrics perspective, exploring the possible directions of future 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 | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.125 | 0.154 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".