Systematic review of the potential role of cannabinoids as antiproliferative agents for urological cancers
Bibliographic record
Abstract
INTRODUCTION: The palliative effects of cannabis sativa (marijuana), which include appetite stimulation, attenuation of nausea and emesis, and pain relief, are well known. The active components of cannabis sativa (cannabinoids) and their derivatives have received growing interest due to their diverse pharmacological activities, such as cell growth inhibition and tumour regression. The aim of this review is to look at the current evidence on the antiproliferative effects of cannabinoids in urological malignancies, including renal, prostate, bladder, and testicular cancers. METHODS: We conducted a systematic review of studies exploring the effect of cannabinoids on tumour activity, including all study types except expert opinions. A formal search was run on Medline database from 1946 to September 2016, along with a hand-search on PubMed for relevant studies. RESULTS: The search yielded a total of 93 studies from Medline and PubMed, of which 23 studies were included in the final analysis. To date, there are various in vitro studies elucidating the potential mechanism of action of cannabinoids for urological cancers, along with population-based studies specifically for testicular malignancies. To date, no clinical trials have been conducted for urological cancer patients. CONCLUSIONS: These results demonstrate that the role of endocannabinoids for urological malignancies is an area of active research. Further research is required not only to evaluate the crosstalk between cancer signaling pathways and cannabinoids, but also large randomized clinical studies with urological patients need to be conducted before cannabinoids can be introduced as potential therapeutic options for urological neoplasms.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".