Anti-Cancer Potential of Cannabinoids, Terpenes, and Flavonoids Present in Cannabis
Bibliographic record
Abstract
In recent years, and even more since its legalization in several jurisdictions, cannabis and the endocannabinoid system have received an increasing amount of interest related to their potential exploitation in clinical settings. Cannabinoids have been suggested and shown to be effective in the treatment of various conditions. In cancer, the endocannabinoid system is altered in numerous types of tumours and can relate to cancer prognosis and disease outcome. Additionally, cannabinoids display anticancer effects in several models by suppressing the proliferation, migration and/or invasion of cancer cells, as well as tumour angiogenesis. However, the therapeutic use of cannabinoids is currently limited to the treatment of symptoms and pain associated with chemotherapy, while their potential use as cytotoxic drugs in chemotherapy still requires validation in patients. Along with cannabinoids, cannabis contains several other compounds that have also been shown to exert anti-tumorigenic actions. The potential anti-cancer effects of cannabinoids, terpenes and flavonoids, present in cannabis, are explored in this literature review.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| 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) | 0.004 | 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".