A Feasibility Assessment of the FDA Adverse Event Reporting System for the Detection of Cannabis-Related Safety Signals
Bibliographic record
Abstract
BACKGROUND: The applicability of spontaneous reporting systems such as the US Food and Drug Administration Adverse Event Reporting System (FAERS) to detect cannabis-related safety signals remains unclear due to the potential for discrepant reporting patterns between pharmaceutical and non-pharmaceutical cannabis-derived products (CDPs). METHODS: We conducted a descriptive analysis of seven groups of CDP reports submitted to FAERS between 1999 and 2023 to investigate product definitions and reporting patterns. We then performed hypothesis-free disproportionality analyses using reporting odds ratio, proportional reporting ratio, and information component for pharmaceutical cannabidiol (CBD) and non-pharmaceutical CBD reports to assess differences in signal detection profiles, potential exposure misclassification, and the influence of reporting context. RESULTS: We identified 42 530 reports related to CDPs, characterized by highly heterogeneous terminology and variable reporting patterns by product type, reflecting the real-world CDP usage. Epidiolex reports often involved pediatric patients, whereas non-pharmaceutical CBD reports were more frequently associated with older adults and concomitant product use. Disproportionality analysis showed divergent signal profiles, with strong seizure-related events predominating for Epidiolex and a broader range of signals, including neoplasm-related and neurological events, observed for non-pharmaceutical CBD. These differences likely reflected variations in CDP indication and utilization and reporting behaviors. CONCLUSIONS: This study showed that signal detection using FAERS has potential feasibility for CDP safety surveillance. However, unique challenges related to exposure definitions, reporting patterns, motivation for utilization, and the need for a robust study design must be addressed to ensure reliable safety signal detection.
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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 | 0.202 | 0.297 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".