The Association between Paternal Cannabinoid Use and Congenital Heart Defects in Offspring
Bibliographic record
Abstract
Cannabis consumption in Canada is rising for both recreational and therapeutic purposes. Research shows that cannabis can impact sperm quality, motility, and volume by binding to receptors on sperm. However, the link between paternal cannabis use and congenital heart defects (CHD) in offspring has not been studied. This ecological study examined Canadian national data on male-household cannabis use and CHD rates per 10,000 births from 2010 to 2020, sourced from Statistics Canada and the Canadian Congenital Anomalies Surveillance System. Using RStudio, Shapiro-Wilk, Spearman’s rank correlation coefficient, and multivariate linear regressions were performed to analyze the relationship, adjusting for household smoking. A p-value below 0.05 was deemed statistically significant. Shapiro-Wilk tests showed that the CHD data did not meet normality (p = 0.004427), while paternal cannabis and smoking data did (p = 0.09368, p = 0.3399). Spearman’s tests found no significant link between paternal cannabis use and CHD in offspring (R² = 0.0251). However, multivariate regressions indicated a correlation between paternal cannabis use and a higher risk of CHD in offspring (β1 = 0.04742, R² = 0.1507). This study, the first to explore this correlation with aggregated data, suggests that paternal cannabis use may be associated with an increased risk of CHD, providing important insights for Canadians.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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".