The Effects of Cannabidiol (CBD) and Δ9-Tetrahydrocannabinol (THC), Alone and in Combination, in Animal Models of Epilepsy and the Comorbidities of Epilepsy
Bibliographic record
Abstract
People with epilepsy often suffer not only from seizures but also from psychiatric comorbidities that accompany the seizures. Recently, cannabidiol (CBD) and combinations of CBD with Δ9-tetrahydrocannabinol (THC) have been proposed for the treatment of epilepsy and the epileptic comorbidities. In the present study, CBD, THC, and combinations of CBD and THC, were evaluated in the mouse maximal electroshock (MES) seizure test, the forced swim test (FST) of antidepressant activity, and the MK-801 model of psychosis. It was found that: 1) CBD and THC were both effective in the MES model, and they were more effective in combination; 2) CBD and THC were ineffective in the FST model, alone or in combination; and 3) CBD and THC were less effective in the MK-801 model but had significant effects in combination. CBD and CBD+THC might be useful in the treatment of epilepsy and possibly psychosis, but not in the treatment of depression.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".