Analysis of literature-derived duplicate records in the FDA Adverse Event Reporting System (FAERS) database
Bibliographic record
Abstract
The FDA Adverse Event Reporting System (FAERS) is a large-scale repository of reports concerning adverse drug events (ADEs). The same published clinical study or report may be reviewed by multiple companies or healthcare professionals and reported separately to the FDA, leading to a significant presence of duplicate reports in FAERS. These duplicate records can result in the identification of false associations between a given drug and an ADE. In this study, we first assessed the consistency of drug and ADE information in FAERS reports from Alzheimer's disease patients. Our findings showed greater congruence in drug-related information compared to ADE-related information, likely due to the greater heterogeneity and variety of terms or phrases used to describe ADEs. We then demonstrated that text comparison methods are effective in identifying duplicate records based on literature citations, testing 10 different comparison functions for their overall efficacy. Token-based methods (such as COSINE, QGRAM, and JACCARD), edit-based approaches (including OSA, LV, and DL), and sequence-based techniques like LCS have proven highly effective in accurately detecting identical publications within free text, demonstrating both high sensitivity and specificity. These results offer valuable insights for identifying duplicate FAERS reports and improving the reliability of detected associations between drugs and ADEs.
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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 | 0.041 | 0.188 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.048 | 0.043 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| 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".