MétaCan
Menu
Back to cohort
Record W4413563310 · doi:10.47611/jsrhs.v13i4.8287

The Association between Paternal Cannabinoid Use and Congenital Heart Defects in Offspring

2024· article· en· W4413563310 on OpenAlexaboutno aff
Thomas Pan, Ario Barin Ostovary, Eric L. Sun

Bibliographic record

VenueJournal of Student Research · 2024
Typearticle
Languageen
FieldMedicine
TopicPrenatal Substance Exposure Effects
Canadian institutionsnot available
Fundersnot available
KeywordsOffspringAssociation (psychology)CannabinoidMedicineInternal medicinePsychologyDevelopmental psychologyGeneticsBiologyPregnancyPsychotherapist

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.492

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.076
GPT teacher head0.399
Teacher spread0.324 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Student ResearchSame topicPrenatal Substance Exposure EffectsFrench-language works237,207