Safety assessment of cenobamate: real-world adverse event analysis from the FAERS database
Bibliographic record
Abstract
Objective: This study aims to analyze adverse drug events (ADEs) associated with cenobamate from the FAERS database, covering the third quarter of 2020 to the second quarter of 2023. Methods: Data related to cenobamate-associated ADEs from the third quarter of 2020 to the second quarter of 2023 were collected. After standardizing the data, various signal quantification techniques, including ROR, MHRA, BCPNN, and MGPS, were employed for analysis. Results: Among 2535 ADE reports where cenobamate was the primary suspected drug, 94 adverse reactions involving 11 different System Organ Class (SOC) categories were identified through the application of four signal quantification techniques. More specifically, neurological disorders and injuries resultant from complications are frequent adverse reactions associated with cenobamate. Conclusion: Our research findings align with established results, affirming the favorable safety profile of cenobamate. Effective prevention of adverse reactions induced by cenobamate can be achieved through the establishment of efficient blood concentration monitoring and dose adjustments.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".