Neurodevelopmental outcomes following prenatal cannabidiol exposure in male and female Sprague Dawley rat offspring
Bibliographic record
Abstract
Cannabis use, including among pregnant women, has increased in recent years due to legalization and other factors in several countries. Cannabidiol (CBD), one of Cannabis' main constituents, is often seen as a natural and safe substance and increasingly used for treating medical conditions such as pain, anxiety, and depression. Women report using CBD during pregnancy to alleviate pregnancy-related symptoms such as nausea, vomiting, and chronic pain. However, few studies exist in the literature regarding the consequences of prenatal CBD exposure. In this study, we treated pregnant rats with CBD (5 and 10 mg/kg; i.p.) once daily from gestational day 6 to 20 and then tracked litter health parameters and conducted neurodevelopmental behavioral tests to assess how treatment affected development. Offspring exposed to CBD prenatally weighed less on postnatal day 1 and gained less weight before weaning on postnatal day 21. Treatment with 10 mg/kg CBD decreased performance in homing behaviour. Subtle changes in righting reflex during the first postnatal week were observed in offspring of litters treated with the higher dose of CBD. These differences resolved by postnatal day 21. No significant differences in a gait test, negative geotaxis, or grip strength were noted. The present results contribute to a growing body of evidence regarding the safety of CBD use during pregnancy and suggest that pregnant people and health care professionals should be cognizant of the potential adverse outcomes of prenatal CBD exposure on growth and development.
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 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) | 0.000 | 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 | 0.001 | 0.002 |
| 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".