Temporal Trends in Semen Quality, Hormone Levels, and Substance Use Among Infertile Men in Pre- and Post-Cannabis Legalization Eras in Canada
Bibliographic record
Abstract
Background: The Cannabis Act (Bill C-45) was enacted in 2018, to legalize and regulate the use, production, and sale of nonmedical cannabis in Canada. While public health and safety implications of cannabis legalization have yet to be elucidated, the wide availability of cannabis necessitates health care providers to be knowledgeable about therapeutic potential and side effects of use. This study aimed to examine the temporal trends over two decades and the impact of the Cannabis Act in Canada, implemented in October 2018, on substance use, semen parameters, and testosterone levels of infertile men. Methods: We conducted a retrospective cohort study from a prospectively maintained database of a single infertility clinic. Demographic, fertility, and substance use history were correlated with semen and hormone assessments. Temporal trends in cannabis use and semen quality between 2001 and 2021 were investigated and compared between pre-cannabis legalization eras (PRCL) and post-cannabis legalization eras (POCL). Results: Our cohort included 11,630 patients (9411 PRCL and 2230 POCL). Cannabis use increased by 8.4% per year ( p <0.001), while alcohol and tobacco consumption declined (0.8% and 1.5% per year, p <0.05 and p =0.004, respectively). Similar trends were noticed in the POCL, with higher rates of cannabis use (22.4% vs. 12.9%, p <0.001) and decreased tobacco and alcohol intake (15.2% vs. 17.7%, p =0.005 and 50.5% vs. 55.2%, p <0.001, respectively) compared to the PRCL group. Semen concentration was lower in the POCL group (24.8±44.8 vs. 28.7±48.3 million/mL, p =0.03). Testosterone did not differ between the cohorts. Comparison between cannabis users ( n =1715) and nonusers ( n =9924) demonstrated a slight increase in sperm motility (25.9%±15.3% vs. 23.9%±15.0%, p =0.002) and decreased sperm concentration among users (27.6±53.5 vs. 23.9±15.0 million/mL, p =0.03). Conclusion: A nearly 10% rise in cannabis use in the POCL era was observed among men being investigated for infertility. Our data suggest cannabis use may be associated with an increase in testosterone, slightly improved sperm motility, and decreased sperm concentration.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".