Cannabis Roots: A Traditional Therapy with Future Potential for Treating Inflammation and Pain
Bibliographic record
Abstract
Abstract Introduction: The roots of the cannabis plant have a long history of medical use stretching back millennia. However, the therapeutic potential of cannabis roots has been largely ignored in modern times. Discussion: In the first century, Pliny the Elder described in Natural Histories that a decoction of the root in water could be used to relieve stiffness in the joints, gout, and related conditions. By the 17th century, various herbalists were recommending cannabis root to treat inflammation, joint pain, gout, and other conditions. There has been a subsequent paucity of research in this area, with only a few studies examining the composition of cannabis root and its medical potential. Active compounds identified and measured in cannabis roots include triterpenoids, friedelin (12.8 mg/kg) and epifriedelanol (21.3 mg/kg); alkaloids, cannabisativine (2.5 mg/kg) and anhydrocannabisativine (0.3 mg/kg); carvone and dihydrocarvone; N -( p -hydroxy- β -phenylethyl)- p -hydroxy- trans -cinnamamide (1.6 mg/kg); various sterols such as sitosterol (1.5%), campesterol (0.78%), and stigmasterol (0.56%); and other minor compounds, including choline. Of note, cannabis roots are not a significant source of Δ 9 -tetrahydrocannabinol (THC), cannabidiol, or other known phytocannabinoids. Conclusion: The current available data on the pharmacology of cannabis root components provide significant support to the historical and ethnobotanical claims of clinical efficacy. Certainly, this suggests the need for reexamination of whole root preparations on inflammatory and malignant conditions employing modern scientific techniques.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".