<i>In utero</i> chronic cannabis exposure is associated with lower total brain volume in the first month of postnatal life
Bibliographic record
Abstract
Background: In utero cannabis exposure is associated with deleterious offspring neural development and behaviors that emerge across the lifespan. We explored if brain morphology differed in neonates exposed and unexposed to cannabis in utero in the first month of life.Objective: To evaluate differences in global and subcortical regional brain volume (in the amygdala and hippocampus) in neonates in the first month of life according to in utero cannabis exposure.Methods: Prospective pre-birth prospective cohort study of mother-infant pairs selected on the basis of prenatal cannabis use in the absence of alcohol, tobacco, or illegal drug use. The presence of cannabinoids using ultra-high performance liquid chromatography-tandem mass spectrometry (LC-MS/MS) was quantified in maternal and neonatal biological samples. Neonatal MRI was conducted to evaluate differences in global and subcortical brain morphology between the exposed and unexposed infants (18 exposed, 21 unexposed). Inverse probability of treatment weighting was utilized in a generalized linear model framework to remove structural confounding bias between exposure groups. Clinical Trial Registration: NCT03718520.Results: The sex distribution of neonates was 43% female. Neonates exposed to cannabis in utero had significantly lower total brain volume (estimated effect size = 26,496.90 mm3, p = .02), independent of confounders including maternal stress, compared to unexposed infants. The unadjusted difference in brain volume was 29,159.82 mm3, p = .05). Regional volumetric differences were not detected in the amygdala or hippocampus.Conclusion: Given the evidence of the adverse effects of exogenous cannabinoids on fetal brain development, it is vital to prioritize prevention and cessation efforts targeting pregnant women.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".