Mapping Levothyroxine related Adverse Events: A Disproportionality Analysis of FAERS Individual Case Safety Reports
Bibliographic record
Abstract
Aim: To compare the proportion of adverse events reports associated with levothyroxine versus other medications and between high-dose (≥100 μg) and low-dose (<100 μg) levothyroxine. A disproportionality analysis was conducted using data from FAERS individual case safety reports (2004–2023) via OpenFDA, with levothyroxine and comparator drugs as primary suspects. Methods: Disproportionality analysis was performed using Reporting Odds Ratio (ROR), Proportional Reporting Ratio (PRR), Bayesian Confidence Propagation Neural Network (BCPNN), and Multi-Item Gamma Poisson Shrinker (MGPS). Signals required confirmation by all four criteria (i.e., IC025 > 0, the lower bound of RR and PRR > 1, and EGBM05 > 2) . Dose-dependent signals were identified using ROR, PRR, and IC025.. Levothyroxine-associated AEs were identified using MedDRA-v27.1 preferred term. Results: Analysis of 45,877 FAERS reports identified 201,473 levothyroxine-related Adverse events and 291 safety signals when comparing levothyroxine with other drugs. Among these, 22 signals were labeled (e.g., irritability [ROR: 13.49], palpitations [ROR: 13.29], alopecia [ROR: 10.25]), while 269 were unexpected (e.g., polyglandular autoimmune syndrome type II [ROR: 118.76], social avoidant behavior [ROR: 48.49]). In a comparison between high-dose (≥100 μg) and low-dose (<100 μg) levothyroxine, 21 signals were identified, including acute kidney injury (ROR: 4.69), increased blood triglycerides (ROR: 3.67), and malabsorption (ROR: 2.90). Conclusions: This study outlines levothyroxine’s safety profile, highlighting both known and unexpected adverse events, with evidence of dose-dependent risks between high- and low-dose levothyroxine. Further pharmacoepidemiologic research is needed to confirm these findings and investigate the underlying mechanisms.
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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 | 0.042 | 0.148 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.014 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".