Completeness of serious adverse drug event reports received by the US Food and Drug Administration in 2014
Bibliographic record
Abstract
PURPOSE: Adverse drug event reports to the US Food and Drug Administration (FDA) remain the primary tool for identifying serious drug adverse effects without adequate existing warnings. We assessed the completeness of reports the FDA received in 2014. METHODS: Serious adverse drug event reports were evaluated for whether they included age, gender, event date, and at least one medical term describing the event in computer excerpts. Report sources were direct reports to the FDA, manufacturer expedited reports about events without adequate warnings, and manufacturer periodic reports about events with existing warnings. RESULTS: In 2014, the FDA received 528,192 new case reports indicating a serious or fatal outcome, 25,038 (4.7%) directly from health professionals and consumers, and 503,154 (95.3%) from drug manufacturers. Overall, 21,595 (86.2%) of serious reports submitted directly to the FDA provided data for all four completeness variables, compared with 271,022 (40.4%) of manufacturer expedited reports and 24,988 (51.3%) of periodic reports. Among manufacturer serious reports, 37.9% lacked age and 46.9% had no event date. Performance by 25 manufacturers submitting 5000 or more reports varied from 24.4% complete on all variables to 67% complete. Patient death cases had the lowest completeness scores in all categories. CONCLUSIONS: By these measures, report completeness from drug manufacturers was poor compared with direct submissions to the agency. The FDA needs to update reporting requirements and compliance policies to help industry capture better adverse event information from new forms of manufacturer interactions with health professionals and consumers. Copyright © 2016 John Wiley & Sons, Ltd.
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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 | 0.036 | 0.136 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".