Cannabis- and Substance-Related Epidemiological Patterns of Chromosomal Congenital Anomalies in Europe: Geospatiotemporal and Causal Inferential Study
Bibliographic record
Abstract
Introduction: Laboratory data link cannabinoid exposure to chromosomal mis-segregation errors. Recent epidemiological reports confirm this link and raise concern that elevated chromosomal congenital anomaly rates (CCAR) may be occurring in Europe which is experiencing increased cannabis use, daily intensity of use and cannabinoid potency. Methods: CCAR data from Eurocat. Drug use data from the European Monitoring Centre for Drugs and Drug Addiction. Income from World Bank. Bivariate, multivariate, panel and geotemporospatial regressions analyzed. Inverse probability weighting of panel models and E-values used as major quantitative causal inferential methodologies. Results: In countries where daily cannabis use was rising the trend for CCA’s was upwards whereas in those where daily use was declining it was usually downwards (p = 0.0002). In inverse probability weighted panel models terms for cannabis metrics were significant for chromosomal disorders, trisomies 21 and 13 and Klinefelters syndrome from p < 2.2 × 10−16. In spatiotemporal models cannabis terms were positive and significant for chromosomal disorders, genetic disorders, trisomies 21, 18 and 13, Turners and Klinefelters syndromes from 4.28 × 10−6, 5.79 × 10−12, 1.26 × 10−11, 1.12 × 10−7, 7.52 × 10−9, 7.19 × 10−7 and 7.27 × 10−7. 83.7% of E-value estimates and 74.4% of minimum E-values (mEV) > 9 including four values each at infinity. Considering E-values: the sensitivity of the individual disorders was trisomy 13 > trisomy 21 > Klinefelters > chromosomal disorders > Turners > genetic syndromes > trisomy 18 with mEV’s 1.91 × 1025 to 59.31; and daily cannabis use was the most powerful covariate (median mEV = 1.91 × 1025). Conclusions: Data indicate that, consistent with reports from Hawaii, Canada, Colorado, Australia and USA, CCARs are causally and spatiotemporally related to metrics and intensity of cannabis exposure, directly impact 645 MB (21.5%) of the human genome and may implicate epigenomic-centrosomal mechanisms.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".