Cannabis Use and Incidence of Testicular Cancer: A 42-Year Follow-up of Swedish Men between 1970 and 2011
Bibliographic record
Abstract
Abstract Background: Given current drug policy reforms to decriminalize or legalize cannabis in numerous countries worldwide, the current study assesses the relation between cannabis use and the development of testicular cancer. Methods: The study included a population-based sample (n = 49,343) of young men ages 18–21 years who underwent conscription assessment for Swedish military service in 1969–1970. The conscription process included a nonanonymous questionnaire eliciting information about drug use. Conscription information was linked to Swedish health and administrative registry data. Testicular cancers diagnosed between 1970 and 2011 were identified by International Classification of Diseases-7/8/9/10 testicular cancer codes in the Swedish National Patient Register, the Cancer Register, or the Cause of Death Register. Cox regression modeling was used to estimate the hazards associated with cannabis use and time to diagnosis of testicular cancer. Results: No evidence was found of a significant relation between lifetime “ever” cannabis use and the subsequent development of testicular cancer [n = 45,250; 119 testicular cancer cases; adjusted HR (aHR), 1.42; 95% confidence interval (CI), 0.83–2.45]. “Heavy” cannabis use (defined as usage of more than 50 times in lifetime, as measured at conscription) was associated with the incidence of testicular cancer (n = 45,250; 119 testicular cancer cases; aHR 2.57; 95% CI, 1.02–6.50). Conclusions: The current study provides additional evidence to the limited prior literature suggesting cannabis use may contribute to the development of testicular cancer. Impact: Emerging changes to cannabis drug policy should consider the potential role of cannabis use in the development of testicular cancer. Cancer Epidemiol Biomarkers Prev; 26(11); 1644–52. ©2017 AACR.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".