Global <scp>COVID</scp>‐19 pandemic and reporting behavior ‐ An analysis of the Food and Drug Administration adverse events reporting system
Bibliographic record
Abstract
PURPOSE: To describe the characteristics of adverse event reporting in the United States (US) Food and Drug Administration Adverse Event Reporting System (FAERS) before and after the outbreak of the COVID-19 pandemic. METHODS: We included all FAERS reports from the US and Canada from November 7, 2019 to July 15, 2020 and divided the study period into three equal time intervals (pre-pandemic, first pandemic, second pandemic). We focused on methotrexate, a broadly used drug unrelated to COVID-19, and (hydroxy)chloroquine, another broadly used drug implicated in COVID-19 treatment. Using descriptive statistics, we compared reporting characteristics before and after the COVID-19 outbreak. RESULTS: During the study period, 366 998 cases (60% female, median age: 59 years) were submitted to FAERS. The daily median number of reports (1796 in the pre-pandemic, 1810 in the second pandemic time interval) and other characteristics remained stable. The daily median number of reports for methotrexate decreased from 28 in the pre-pandemic to 15 in the second pandemic time interval, with no considerable differences in other characteristics. The daily median number of reports for (hydroxy)chloroquine increased slightly from 1 in the pre-pandemic to 3 in the second pandemic time interval, while there were also changes in the demographics of cases and an increase in the proportion of cases reported by health professionals. CONCLUSIONS: The overall reporting to FAERS did not change after the outbreak of the COVID-19 pandemic. However, some stimulated reporting was observed for (hydroxy)chloroquine, highlighting the need for caution when conducting pharmacovigilance analyses with substances related to COVID-19.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".