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Record W3103642155 · doi:10.1186/s40360-020-00450-1

Broad Spectrum epidemiological contribution of cannabis and other substances to the teratological profile of northern New South Wales: geospatial and causal inference analysis

2020· article· en· W3103642155 on OpenAlexaboutno aff
Albert Stuart Reece, Gary Kenneth Hulse

Bibliographic record

VenueBMC Pharmacology and Toxicology · 2020
Typearticle
Languageen
FieldMedicine
TopicCannabis and Cannabinoid Research
Canadian institutionsnot available
Fundersnot available
KeywordsCannabisMedicineDemographyEpidemiologyPublic healthEnvironmental healthInternal medicinePsychiatry

Abstract

fetched live from OpenAlex

Abstract Background Whilst cannabis commercialization is occurring rapidly guided by highly individualistic public narratives, evidence that all congenital anomalies (CA) increase alongside cannabis use in Canada, a link with 21 CA’s in Hawaii, and rising CA’s in Colorado indicate that transgenerational effects can be significant and impact public health. It was therefore important to study Northern New South Wales (NNSW) where cannabis use is high. Methods Design: Cohort. 2008–2015. Setting: NNSW and Queensland (QLD), Australia. Participants. Whole populations. Exposures. Tobacco, alcohol, cannabis. Source: National Drug Strategy Household Surveys 2010, 2013. Main Outcomes. CA Rates. NNSW-QLD comparisons. Geospatial and causal regression. Results Cardiovascular, respiratory and gastrointestinal anomalies rose with falling tobacco and alcohol but rising cannabis use rates across Queensland. Maternal age NNSW-QLD was not different (2008–2015: 4265/22084 v. 96,473/490514 > 35 years/total, Chi.Sq. = 1.687, P = 0.194). A higher rate of NNSW cannabis-related than cannabis-unrelated defects occurred (prevalence ratio (PR) = 2.13, 95%C.I. 1.80–2.52, P = 3.24 × 10 − 19 ). CA’s rose more potently with rising cannabis than with rising tobacco or alcohol use. Exomphalos and gastroschisis had the highest NNSW:QLD PR (6.29(2.94–13.48) and 5.85(3.54–9.67)) and attributable fraction in the exposed (84.11%(65.95–92.58%) and 82.91%(71.75–89.66%), P = 2.83 × 10 − 8 and P = 5.62 × 10 − 15 ). In multivariable geospatial models cannabis was significantly linked with cardiovascular (atrial septal defect, ventricular septal defect, tetralogy of Fallot, patent ductus arteriosus), genetic (chromosomal defects, Downs syndrome), gastrointestinal (small intestinal atresia), body wall (gastroschisis, diaphragmatic hernia) and other (hypospadias) (AVTPCDSGDH) CA’s. In linear modelling cannabis use was significantly linked with anal stenosis, congenital hydrocephalus and Turner syndrome (ACT) and was significantly linked in borderline significant models (model P < 0.1) with microtia, microphthalmia, and transposition of the great vessels. At robust and mixed effects inverse probability weighted multivariable regression cannabis was related to 18 defects. 16/17 E-Values in spatial models were > 1.25 ranging up to 5.2 × 10 13 making uncontrolled confounding unlikely. Conclusions These results suggest that population level CA’s react more strongly to small rises in cannabis use than tobacco or alcohol; cardiovascular, chromosomal, body wall and gastrointestinal CA’s rise significantly with small increases in cannabis use; that cannabis is a bivariate correlate of AVTPCDSGDH and ACT anomalies, is robust to adjustment for other substances; and is causal.

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)0.000
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 score0.029
Threshold uncertainty score0.520

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.040
GPT teacher head0.350
Teacher spread0.310 · 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

Citations38
Published2020
Admission routes1
Has abstractyes

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