An Overview of Galenic Preparation Methods for Medicinal Cannabis
Bibliographic record
Abstract
In recent years, the Cannabis plant (Cannabis sativa L.) has been rediscovered as a source of new medicines around the world. Despite the fact that a number of registered medicines have been developed on the basis of purified cannabis components, there is a rapid increasing acceptance and use of cannabis in its herbal form. Licensed producers of high quality cannabis plants now operate in various countries including The Netherlands, Canada, Israel, and Australia, and in many US states. The legal availability of cannabis flowers allows to prescribe and prepare different cannabis galenic preparations by pharmacists. It is believed that synergy between cannabis components, known as “entourage effect”, may be responsible for the superior effects of using herbal cannabis versus isolated compounds. So far, only a few cannabis components have been properly characterized for their therapeutic potential, making it unclear which of the isolated compounds should be further developed into registered medicines. Until such products become available, simple and accessible galenic preparations from the cannabis plant could play an important role. In cannabis, phytochemical and pharmacological attention has been attributed mainly to four major cannabinoids (Δ9- tetrahydrocannabinol, cannabidiol, cannabigerol and cannabichromene) and to terpene components. This means a basic knowledge of these compounds and their bioavailability in different administration forms is useful for producers as well as prescribers of galenic preparations. This work will outline the most important aspects of cannabinoids and terpenes, and their behaviors during preparation and use of various administration forms including vaporizing, cannabis oils and extracts, tea, and skin creams.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.009 |
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".