Neurodevelopmental Effects of Prenatal Cannabidiol Exposure on the Offspring of Rats in the Postnatal Period
Bibliographic record
Abstract
Cannabis use during pregnancy has recently increased worldwide. Cannabidiol (CBD), the main non-intoxicating compound in Cannabis, is often seen as a natural substance and has been used for the treatment of several health conditions. During pregnancy, women might choose to use CBD to treat very common pregnancy-related symptoms, such as nausea and vomiting. However, there is very little evidence regarding the safety of CBD use during pregnancy and the possible outcomes to maternal and fetal health. In this context, we tested the effects of prenatal CBD exposure on pregnancy outcomes, offspring physical health and neurodevelopment. Pregnant rats were treated by intraperitoneal injection with either a drug vehicle solution (1:1:18 ethanol:kolliphor:PBS), 5 mg/kg CBD or 10 mg/kg CBD during gestational days 6 to 20. Offspring physical health was assessed until weaning on post-natal day (PND) 21. Different neurodevelopmental tests were conducted from PND3 to PND21 to measure the development of neurological reflexes and postural mechanisms. Prenatal CBD exposure was associated with a lower body weight in offspring and a delay in the development of reflexes in early stages after birth. These findings contribute to the current evidence available on the consequences of in utero CBD exposure and brings light to the need for further research in the area.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".