COVID-19 vaccines and autoimmune disorders: A scoping review
Bibliographic record
Abstract
BackgroundUpon the global COVID-19 vaccination campaign, unprecedented in the history of public health, concerns have emerged regarding potential associations between vaccination and autoimmune disorders. Historical research has long identified mechanisms by which vaccines might trigger or unmask autoimmune processes. However, systematic synthesis of evidence concerning COVID-19 vaccines and autoimmunity remains limited.ObjectiveTo review the literature on associations between COVID-19 vaccination and autoimmunity, focusing on six conditions: Graves' disease, Hashimoto's thyroiditis, multiple sclerosis, rheumatoid arthritis, systemic lupus erythematosus, and type 1 diabetes mellitus.MethodsWe conducted a scoping review of 109 studies published in 2022, retrieved from PubMed and the WHO COVID-19 databases. Inclusion criteria encompassed English-language articles reporting empirically verifiable clinical manifestations of autoimmune disease associated with any COVID-19 vaccine, without restrictions by population, geography, or study type.ResultsAcross 109 included studies, relapses or flares in patients with autoimmune disorders were reported in nearly 60% of studies, while about one-quarter described new-onset autoimmune disorders in persons without prior autoimmunity. Several mechanisms of action linking COVID-19 vaccination and autoimmune disorders were reported, such as autoimmune inflammatory syndrome induced by adjuvants, molecular mimicry, bystander immune activation, and interactions with immunosuppressive and disease modifying therapies. Serious adverse events, though less common than mild or moderate ones, were also reported. General and population-specific vaccine efficacy were claimed but empirical support was often lacking.ConclusionsThis review highlights the substantial patterns of reported associations of autoimmune disorders following COVID-19 vaccination, in patients with and without prior autoimmunity. The general and population-specific benefits of vaccination are claimed, but evidence for them is lacking. A proper evaluation of risks and benefits is needed to support vaccination recommendations given the reported associations between it and autoimmune disorders.
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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 | 0.007 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.019 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".