The association of bladder cancer and Cannabis: A systematic review
Bibliographic record
Abstract
OBJECTIVE: To assess the association between Cannabis use and bladder cancer. METHODS: A systematic literature review was performed using studies published in electronic databases including PubMed, MEDLINE, and Google Scholar. Due to the scarcity of literature on this topic, the search was not limited to a specific design, year of publication, or human studies. The studies were screened by two reviewers in the following steps; first, the studies were discovered according to the predetermined search strategy; second, the unrelated studies and duplicates were eliminated by screening the abstracts, titles, and keywords; third, the full text of relevant and eligible papers were critically appraised and assessed for the risk of bias using the respective tool. The two review authors independently assessed the risk of bias and outcome levels using the Newcastle-Ottawa Scale for the outcomes in observational studies. Any disagreements were settled by a third party. RESULTS: The search strategy yielded 39 research articles. After removing 21 duplicates, 18 publications were eligible for title and abstract review. Thirteen studies were found to be irrelevant and subsequently excluded. Only three full-text articles were evaluated and included in the qualitative synthesis. CONCLUSIONS: The role of Cannabis in bladder cancer has been seldom studied. The small number of studies show contradictory findings; potential carcinogenic versus protective effect. The growing interest in Cannabis use after legalization necessitates further investigations with a robust design to assess the long-term effect of Cannabis on bladder cancer.
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 | 0.011 | 0.046 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 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".