Retracted: Assessing the Causal Association Between COVID-19 and Graves Disease: Mendelian Randomization Study
Post-publication record
OpenAlex flags this work as retracted, but it carries no matching Retraction Watch record in this frame.
Bibliographic record
Abstract
Background: Graves disease (GD) is an autoimmune thyroid disorder characterized by hyperthyroidism and autoantibodies. The COVID-19 pandemic has raised questions about its potential relationship with autoimmune diseases like GD. Objective: This study aims to investigate the causal association between COVID-19 and GD through Mendelian randomization (MR) analysis and assess the impact of COVID-19 on GD. Methods: We conducted an MR study using extensive genome-wide association study data for GD and COVID-19 susceptibility and its severity. We used stringent single nucleotide polymorphism selection criteria and various MR methodologies, including inverse-variance weighting, MR-Egger, and weighted median analyses, to assess causal relationships. We also conducted tests for directional pleiotropy and heterogeneity, as well as sensitivity analyses. Results: The MR analysis, based on the largest available dataset to date, did not provide evidence supporting a causal relationship between COVID-19 susceptibility (odds ratio [OR] 0.989, 95% CI 0.405-2.851; P=.93), COVID-19 hospitalization (OR 0.974, 95% CI 0.852-1.113; P=.70), COVID-19 severity (OR 0.979, 95% CI 0.890-1.077; P=.66), and GD. Tests for directional pleiotropy and heterogeneity, as well as sensitivity analyses, supported these findings. Conclusions: This comprehensive MR study does not provide sufficient evidence to support a causal relationship between COVID-19 and the onset or exacerbation of GD. These results contribute to a better understanding of the potential association between COVID-19 and autoimmune diseases, alleviating concerns about a surge in autoimmune thyroid diseases due to the pandemic. Further research is warranted to explore this complex relationship thoroughly.
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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 | 0.169 | 0.448 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.008 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".