Bibliographic record
Abstract
Epilepsy is a disorder in which several recurrent seizures occur, and despite the fact that there are over twenty anti-seizure drugs available, more than 30% of people with epilepsy continue to have seizures (Friedman & Devinsky, 2015; Kolb & Whishaw, 2009). Many researchers have turned to marijuana, specifically the constituent cannabidiol (CBD), as they search for new solutions to effectively help this treatment-resistant form of epilepsy. The purpose of this paper is to provide an assessment between the relationship of marijuana and epilepsy. I will review a total of six studies, including one case study, and one meta-analysis. A considerable amount of controversy surrounds this topic, as marijuana is illegal in many parts of the world, and many researchers are undecided as to whether its legalization will be beneficial or not. In spite of this disagreement, most researchers believe that marijuana, specifically CBD, has shown some evidence in regard to the positive health benefits and reduction of seizures in epilepsy. Future analysis requires high quality and reliable studies which can continue to further our understanding of the relationship between marijuana and epilepsy.
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 | 0.006 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.005 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".