Adverse events of the thyroid peroxidase inhibitor methimazole in the treatment of hyperthyroidism: a comprehensive analysis from the first quarter of 2004 to the first quarter of 2025
Bibliographic record
Abstract
Purpose: The primary objective of this study is to systematically monitor and comprehensively characterize adverse events (AEs) associated with methimazole, which is utilized in the treatment of hyperthyroidism. Method: A comprehensive analysis of the US FDA Adverse Event Reporting System (FAERS) database was conducted, covering data from the first quarter of 2004 to the first quarter of 2025. Multiple signal detection algorithms, including the ROR, PRR, BCPNN, and EBGM, were employed for conduct disproportionality analysis to efficiently mine the data and accurately identify signals associated with AEs related to methimazole. Result: After analyzing 1,908 patient cases with 6,449 reported AEs linked to methimazole, the study confirmed AEs like agranulocytosis, pyrexia, hypothyroidism, and drug-induced liver injury aligning with the drug's package insert. Interestingly, several previously unreported AEs, such as premature baby, polyarthritis, pleural effusion, septic shock, cholestasis and jaundice cholestatic were identified. The findings indicate potential unrecognized AEs and highlight the importance of continued pharmacovigilance and rigorous drug safety surveillance. The median onset time of methimazole related AEs was 27 days (interquartile range [IQR]: 5-58 days), indicating that the majority of cases occurred early after initiating use methimazole. Conclusion: This study confirms that agranulocytosis, pyrexia, exposure during pregnancy, and hyperthyroidism are AEs associated with methimazole use. It also identifies new safety signals, including premature infants, polyarthritis, pleural effusion, septic shock, cholestasis, and cholestatic jaundice, that warrant further investigation. These findings support deeper analysis of the relationship between methimazole and adverse events and may help improve patient safety and clinical outcomes.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".