Urogenital Malignancy and Cannabis Use: A Narrative Review
Bibliographic record
Abstract
Background Cannabis is the most commonly used illicit drug worldwide. An increasing number of jurisdictions are legalising cannabis for both medicinal and recreational use. The changing cannabis market has resulted in both an increase in the number of people consuming these compounds, and an increase in the frequency and quantity of cannabis being used. Endogenous and exogenous cannabinoids act on receptors across the entire body including the genitourinary system; however, there is a paucity of understanding of how cannabinoids affect genitourinary malignancy. Objective To present a narrative review of the available literature detailing the relationship between cannabis and the incidence, diagnosis, and management of genitourinary malignancy. Methods A comprehensive search was undertaken using the Ovid MEDLINE, Ovid Embase, and Cochrane Central Register of Controlled Trials (CENTRAL) up to July 2021. Studies included case reports, case series, casecontrol studies, and in vitro studies. Results The search identified 40 studies in total: 8 described the relationship between cannabis and testicular carcinoma, 20 related to prostate cancer, 5 to bladder cancer, 5 to renal cancer, 1 to penile cancer, and 1 study examined testicular carcinoma, renal cell carcinoma, bladder cancer, and prostate cancer. Conclusions Cannabis use has been linked to an increased risk of developing testicular tumours, whilst the evidence for bladder cancer is mixed. There is no apparent increase in risk for prostate cancer, penile cancer, or renal cell carcinoma; however, this evidence was based on a very small number of patients. There remains a lack of understanding of the relationship between cannabis and genitourinary malignancy. With an expected increase in cannabis use, monitoring for testicular tumour plus efforts to further understand its effects upon the genitourinary tract will aid diagnosis and management.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".