Trends in Publications on Medical Cannabis from the Year 2000
Bibliographic record
Abstract
Widespread use of cannabis as a drug and passage of legislation on its use should lead to an increase in the number of scientific publications on cannabis. The aim of this study was to compare trends in scientific publication for papers on medical cannabis, papers on cannabis in general, and all papers between the years 2000 and 2017. A search of PubMed and Web of Science was conducted. The overall number of scientific publications in PubMed increased 2.5-fold. In contrast, the number of publications on cannabis increased 4.5-fold and the number of publications on medical cannabis increased almost 9-fold. The number of publications on medical cannabis in Web of science increased even more (10-fold). The most significant number of publications was in the field of psychiatry. In the fields of neurology and cancer treatment there was a significant increase in the years 2011-2013. There was a rise in the number of publications on children and the elderly after 2013. The specific indications with the largest number of publications were HIV (261), chronic pain (179), multiple sclerosis (118), nausea and vomiting (102), and epilepsy (88). More than half of the publications on medical cannabis originated from the United States, followed by Canada. More than 66% of the publications were original studies. The spike in the number of scientific publications on medical cannabis since 2013 is encouraging. In light of this trend the authors expect an even greater increase in the number of publications in this area in coming years.
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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 | 0.007 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.060 | 0.064 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".