COVID-19 vaccines and autoimmune disorders: A scoping review protocol
Bibliographic record
Abstract
Abstract Background Two years into the global vaccination program, important questions about the association between COVID-19 vaccines and autoimmune diseases have arisen. A growing number of reports have documented associations between COVID-19 vaccination and autoimmunity, suggesting, for example, a causal link between vaccination and new-onset and/or relapsing autoimmune disorders such as type 1 diabetes mellitus, rheumatoid arthritis, multiple sclerosis, systemic lupus erythematosus, Graves disease, and Hashimoto’s thyroiditis. These autoimmune phenomena have occurred with various COVID-19 vaccines and research is required to elucidate the underlying mechanisms and causal directions, for example, whether persons with no history of autoimmune disorders may experience them upon vaccination or persons with autoimmune disorders may experience exacerbation or new adverse events post-vaccination. Methods and analysis Specific objectives of this scoping review will address the following questions: Can COVID-19 vaccination trigger and/or exacerbate autoimmune disorders? Are persons with autoimmune disorders at higher risk of experiencing additional autoimmune disorders? What are the mechanisms connecting autoimmune disorders with COVID-19 vaccination? Can COVID-19 vaccination interact with immunosuppressive therapy in persons with autoimmune disorders? Does the risk of autoimmune disorders following COVID-19 vaccination differ by vaccine type, age, gender, or other still unidentified characteristics (e.g., SES)? What is the consensus of care concerning COVID-19 vaccination in persons with autoimmune disorders and what evidence informs it? Our review will follow Arksey and O’Malley’s (2005) framework, enhanced by Levac et al.’s team-based approach (2010), and adhering to the recommendations of the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) Checklist. To capture the broadest range of perspectives on the phenomenon of interest, data will be synthesized through numerical summaries describing general characteristics of included studies and thematic analysis. Subgroup analysis of primary outcomes will be performed to compare findings according to 1) the previous existence of autoimmune disorder, 2) the presence of relevant co-morbidities, 3) vaccine type; and other relevant factors that we may encounter as the research proceeds. Significance COVID-19 has triggered the largest vaccination campaign in history, targeting literally the global human community. Drug safety is a crucial aspect of any medical intervention, critical to a proper assessment of the balance of risks and benefits. Our investigation should yield information useful to improve medical and public health practice in multiple ways, including assisting in clinical decision-making, policy development, and ethical medical practice.
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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 | 0.044 | 0.064 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.011 | 0.010 |
| Bibliometrics | 0.025 | 0.019 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.040 | 0.005 |
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".