High-quality reports and their characteristics in the Japanese Adverse Drug Event Report database (JADER)
Bibliographic record
Abstract
Purpose: Spontaneous adverse drug reaction reporting is the foundation of postmarketing drug safety monitoring. The present study aimed to analyze and clarify the quality and characteristics of the Japanese Adverse Drug Event Report database (JADER) using the World Health Organization (WHO) documentation grading scheme and the vigiGrade completeness score. The characteristics of reports were described using both schemes simultaneously. The way of proper use of these two schemes was explored. Methods: The WHO documentation grading scheme and the vigiGrade completeness score were applied to the same dataset (JADER202001 dataset). Reports classified as high-quality under both assessment criteria were extracted, and the characteristics of these reports were analyzed. Results: Of the 607,361 adverse drug reaction reports analyzed, 52.8% were ‘well-documented reports’ with a vigiGrade completeness score >0.8. Under the WHO documentation grading scheme, 328,702 reports (54.1%) were Grade 2 and 5,178 (0.9%) were Grade 3 (including rechallenge information). Among well-documented Grade 3 reports, classified as the highest quality, a high proportion of the adverse drug reaction reports were related to disorders of hematopoietic function resulting from anticancer drugs. Because a high proportion of the reports with rechallenge information were for anticancer drugs as suspect drugs, the WHO documentation grading scheme tended to extract reports regarding anticancer drugs as high quality. Conclusions: We conclude that the two schemes need to be used appropriately, depending on the purpose of analysis, the target adverse drug reactions, and suspect drugs.
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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 | 0.018 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".