Use of cannabis in urological cancer patients: A review to evaluate risk for cancer development, therapeutic use, and symptom management
Bibliographic record
Abstract
INTRODUCTION: Recent recreational legalization of cannabis has resulted in an increased interest in the therapeutic effects of cannabis use in cancer patients, with reports of its use in symptom management and as a risk factor for cancer development. The objective of this review was to evaluate the literature on the association of cannabis use with the risk of cancer development, symptom management, and therapeutic management in the urological cancer (UC) patient population. METHODS: A systematic search of databases and trial registries for papers published from January 1947 to March 2020 on cannabis, symptom and therapeutic management, and cancer development in UC patients was conducted. After screening of full-text articles, data were extracted for evaluation. Studies were eligible if they were in the clinical setting, included ≥5 UC patients, reported use of any cannabis variant, and were written in English. RESULTS: The search retrieved 2456 abstracts, of which 48 full-text articles were reviewed and 21 included in the review. Low-level evidence suggested a correlation between cannabis use and risk for development of testicular cancer. Some support existed for using cannabis for cancer pain and chemotherapy-induced nausea. There was inadequate evidence to substantiate cannabis use as a therapeutic agent for management of UCs. A lack of high-level evidence and robust methodology of the studies limited evaluation of the findings. CONCLUSIONS: Given the paucity of data on cannabis use for therapeutic purposes in UC, large, prospective trials with adequate followup times to observe the effect of cannabis use on UCs are warranted to improve the evidence base.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".