Exploring the Association between Cannabis Use and Testosterone Levels in Men Receiving Methadone Maintenance Treatment
Bibliographic record
Abstract
Cannabis and opioids are substances that affect reproductive health. Opioids suppress testosterone and studies have shown that cannabis may increase testosterone. However, there is minimal research describing the endocrine effects of concurrent cannabis and opioid use. We hypothesize that cannabis use improves opioid-induced testosterone suppression. To test this hypothesis, we used cross-sectional data from a prospective cohort study including 122 men enrolled in methadone maintenance treatment (MMT). We measured serum testosterone with an enzyme-linked immunosorbent assay at study enrolment. Urine drug screens were collected for 15 months and identified 52.5% of participants (n = 64) as cannabis users. The association between cannabis use and testosterone level was examined using regression models with serum testosterone as the dependent variable. In our multivariable regression, methadone dose was associated with lower serum testosterone (β = −0.003, 95% CI-0.005, −0.001, p = 0.003). However, neither cannabis use as a dichotomous variable nor the percentage of cannabis-positive urine drug screens were significantly associated with serum testosterone (β = 0.143, 95% CI −0.110, 0.396, p = 0.266, and β = 0.002, 95% CI > −0.001, 0.005, p = 0.116, respectively). Therefore, it does not appear that cannabis has an association with testosterone levels in men on MMT.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".