Use of non-medical cannabis in epilepsy: A scoping review
Bibliographic record
Abstract
Objective: The use of medical cannabis among people with epilepsy (PWE) has been garnering increasing interest. In this scoping review, we aimed to summarize the literature on recreational/non-medical cannabis (NMC) use in PWE, focusing on the experience, habits, and beliefs of PWE regarding NMC. Methods: Four databases (OVID Medline, OVID Embase, Ovid APA PsycInfo, and Web of Science) were searched for studies describing NMC use in PWE. NMC was defined as cannabis products procured from sources other than by prescription. Studies that consisted in original research and that detailed the experience, habits, and/or beliefs of PWE regarding NMC use were included in the analysis. Data pertaining to study identification, demographics, NMC use, and epilepsy characteristics were extracted. Descriptive statistical analyses and reflexive thematic analyses were performed to map these data. Results: In total, 3,228 records were screened, and 66 were included for analysis: 45 had mainly adult samples, whereas 21 had mainly pediatric samples. Most studies were published after 2010, originated from the USA, and were cross-sectional. The median number of PWE using cannabis in these studies was 24.5 (1-37,945). No studies showcased elderly PWE, and most had predominantly Caucasian samples. The lifetime prevalence of NMC use in PWE was variable, ranging between 0.69 and 76.8%. Factors frequently associated with NMC use in PWE were male sex, younger adult age, and lower education status. Children with epilepsy took NMC primarily for seizure control, using high CBD/THC ratios, and only orally. Adults with epilepsy took NMC for various reasons including recreationally, using variable CBD/THC ratios, and predominantly through smoking. The majority of PWE across all studies perceived that NMC aided in seizure control. Other aspects pertaining to NMC use in PWE were rarely reported and often conflicting. Conclusion: The literature on NMC use in PWE is sparse and heterogeneous, with many salient knowledge gaps. Further research is necessary to better understanding the experience, habits, and beliefs of PWE pertaining to NMC.
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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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".