Completeness of spontaneously reported adverse drug reactions in 4 databases
Bibliographic record
Abstract
AIMS: To assess the completeness of information provided in adverse drug reaction (ADR) reports in 4 spontaneous report databases. METHODS: The study was conducted using freely accessible ADR reports from the Canada Vigilance Adverse Reaction Online Database, the US Food and Drug Administration Adverse Event Reporting System (FAERS) database, the UK Yellow Card Scheme and the Danish Medicines Agency ADR Database, covering the period 2014-2023. The variables used to evaluate the completeness of ADR reports were selected based on the vigiGrade completeness tool. Descriptive statistics were used to report the completeness of information in each ADR report, while chi-square tests were used to analyse differences in completeness by seriousness of report. RESULTS: In total, 290 079 individual case safety reports were analysed: 4289 from the Canadian database; 269 763 from the FAERS database; 13 624 from the UK Yellow Card Scheme; and 2403 from the Danish database. The most frequently completed information was the primary reporter, with nearly 100% completeness. The most frequently omitted information was the route of administration, with completion scores of 44% in the UK Yellow Card Scheme, 55% in the Danish database and 58% in the Canadian database. In FAERS, the event date was the most frequently omitted information with a completeness rate of 50%. CONCLUSION: The completeness of information varied across different variables, with information frequently omitted for route of administration and event date. Enhancing awareness about providing complete information during ADR reporting is essential for collecting complete data and allowing 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 | 0.104 | 0.335 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.017 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".