Patterns of Cannabis- and Substance-Related Congenital General Anomalies in Europe: A Geospatiotemporal and Causal Inferential Study
Bibliographic record
Abstract
Introduction: Recent series of congenital anomaly (CA) rates (CARs) have showed the close and epidemiologically causal relationship of cannabis exposure to many CARs. We investigated these trends in Europe where similar trends have occurred. Methods: CARs from EUROCAT. Drug use from European Monitoring Centre for Drugs and Drug Addiction. Income data from World Bank. Results: CARs were higher in countries with increasing daily use overall (p = 9.99 × 10−14, minimum E-value (mEV) = 2.09) and especially for maternal infections, situs inversus, teratogenic syndromes and VACTERL syndrome (p = 1.49 × 10−15, mEV = 3.04). In inverse probability weighted panel regression models the series of anomalies: all anomalies, VACTERL, foetal alcohol syndrome, situs inversus (SI), lateralization (L), and teratogenic syndromes (TS; AAVFASSILTS) had cannabis metric p-values from: p < 2.2 × 10−16, 1.52 × 10−12, 1.44 × 10−13, 1.88 × 10−7, 7.39 × 10−6 and <2.2 × 10−16. In a series of spatiotemporal models this anomaly series had cannabis metric p-values from: 8.96 × 10−6, 6.56 × 10−6, 0.0004, 0.0019, 0.0006, 5.65 × 10−5. Considering E-values, the cannabis effect size order was VACTERL > situs inversus > teratogenic syndromes > FAS > lateralization syndromes > all anomalies. 50/64 (78.1%) E-value estimates and 42/64 (65.6%) mEVs > 9. Daily cannabis use was the strongest predictor for all anomalies. Conclusion: Data confirmed laboratory, preclinical and recent epidemiological studies from Canada, Australia, Hawaii, Colorado and USA for teratological links between cannabis exposure and AAVFASSILTS anomalies, fulfilled epidemiological criteria for causality and underscored importance of cannabis teratogenicity. VACTERL data are consistent with causation via cannabis-induced Sonic Hedgehog inhibition. TS data suggest cannabinoid contribution. SI&L data are consistent with results for cardiovascular CAs. Overall, these data show that cannabis is linked across space and time and in a manner which fulfills epidemiological criteria for causality not only with many CAs, but with several multiorgan teratologic syndromes. The major clinical implication of these results is that access to cannabinoids should be tightly restricted in the interests of safeguarding the community’s genetic heritage to protect and preserve coming generations, as is done for all other major genotoxins.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".