Cannabinoids and Adverse Convulsive Effects: A Pharmacovigilance and Addictovigilance Analysis of Cases Reported in France
Bibliographic record
Abstract
BACKGROUND: Seizures after the use of cannabinoids are reported, but no precise descriptions of the characteristics of subjects and factors that may trigger seizures are available. OBJECTIVES: To study the characteristics and circumstances associated with the occurrence of seizures in individuals using cannabinoids for medical or recreational purposes. METHODS: A retrospective analysis of spontaneous reports of adverse drug effects issued by the French pharmacovigilance and addictovigilance systems, and by manufacturers, extracted data from the Eudravigilance database (01/01/1985-21/07/2023). The request used the broad MedDRA SMQ term 'convulsive', with all products containing cannabinoids (THC, CBD, cannabis or natural cannabinoids). RESULTS: Among 4296 notifications with cannabinoids, 130 (3%) reports of convulsive effects were analysed: 29 cases (23.3%) related to medical use (27 CBD, 1 THC and 2 combined THC/CBD preparations) and 98 (75.4%) related to recreational use. The median age was 29.0 years (min-max: 3-75), 78.7% were men and 81.1% were serious cases. Among the recreational users, 38.8% used Cannabis sativa with a history of epilepsy, and 68.4% of them were taking antiepileptics. In total, 67.7% of individuals had at least one risk factor for seizures, i.e., 31.0% among medical users and 78.6% among recreational users. The main risk factors with medical use were inefficacy of CBD (17.2%), fatigue (13.8%) and concomitant epileptogenic medications (10.3%). The main risk with recreational use was concomitant epileptogenic medications (39.8%), consumption of illicit drugs (33.7%) and alcohol (32.7%). CONCLUSION: This analysis demonstrates the importance of alerting cannabinoid users, particularly recreational cannabis users and those with a history of epilepsy, about seizure-associated risks. Moreover, educational information should be provided together with the prescription of licensed cannabinoids and medical cannabis.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".