Utility and limitations of the FDA adverse events reporting system public dashboard for safety analyses: a case study with vesicular monoamine transporter 2 inhibitors
Bibliographic record
Abstract
INTRODUCTION: The United States Food and Drug Administration (FDA) requires post-marketing surveillance of approved drugs, and pharmaceutical manufacturers maintain comprehensive programs that include adverse event monitoring, internal safety assessments, and reporting to the FDA Adverse Events Reporting System (FAERS). AREAS COVERED: This report provides an overview of FAERS within the broader framework of post-marketing surveillance by pharmaceutical manufacturers. It also identifies several limitations to FAERS public dashboard data for safety analyses. A PubMed search for published findings of FAERS safety analyses with vesicular monoamine transporter 2 (VMAT2) inhibitors provide a case study that illustrates the need for careful interpretation based on the limitations of the FAERS database. EXPERT OPINION: Using a case study of VMAT2 inhibitors, we identified factors in data quality and manufacturer pharmacovigilance programs that must be considered when interpreting published analyses of FAERS public safety data. The application of artificial intelligence methodologies may prove helpful in identifying novel safety signals more accurately and more rapidly. At the same time, as clinicians consider individual treatment choices with their patients, discussion of safety data from the FAERS public dashboard should be contextualized within each drug's known safety profile.
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 | 0.117 | 0.324 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".