Cannabinoid and substance relationships of European congenital anomaly patterns: a space-time panel regression and causal inferential study
Bibliographic record
Abstract
Abstract With reports from Australia, Canada, USA, Hawaii and Colorado documenting a link between cannabis and congenital anomalies (CAs), this relationship was investigated in Europe. Data on 90 CAs were accessed from Eurocat. Tobacco and alcohol consumption and median household income data were from the World Bank. Amphetamine, cocaine and last month and daily use of cannabis from the European Monitoring Centre for Drugs and Drug Addiction. Cannabis herb and resin Δ9-tetrahydrocannabinol concentrations were from published reports. Data were processed in R. Twelve thousand three hundred sixty CA rates were sourced across 16 nations of Europe. Nations with a higher or increasing rate of daily cannabis use had a 71.77% higher median CA rates than others [median ± interquartile range 2.13 (0.59, 6.30) v. 1.24 (0.15, 5.14)/10 000 live births (P = 4.74 × 10−17; minimum E-value (mEV) = 1.52]. Eighty-nine out of 90 CAs in bivariate association and 74/90 CAs in additive panel inverse probability weighted space-time regression were cannabis related. In inverse probability weighted interactive panel models lagged to zero, two, four and six years, 76, 31, 50 and 29 CAs had elevated mEVs (< 2.46 × 1039) for cannabis metrics. Cardiovascular, central nervous, gastrointestinal, genital, uronephrology, limb, face and chromosomalgenetic systems along with the multisystem VACTERL syndrome were particularly vulnerable targets. Data reveal that cannabis is related to many CAs and fulfil epidemiological criteria of causality. The triple convergence of rising cannabis use prevalence, intensity of daily use and Δ9-tetrahydrocannabinol concentration in herb and resin is powerfully implicated as a primary driver of European teratogenicity, confirming results from elsewhere.
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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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".