COVID-19 vaccines and autoimmune disorders: A scoping review protocol
Bibliographic record
Abstract
Two years into the global vaccination campaign, important questions about COVID-19 vaccines and autoimmune disorders have arisen. A growing number of reports have documented associations between vaccination and autoimmunity, and research is needed to elucidate the nature of these linkages as well as the mechanisms and causal directions (i.e., 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, autoimmune or not, post-vaccination). This scoping review will follow Arksey and O'Malley's framework, which is enhanced by Levac et al.'s team-based approach, to address the relationship between COVID-19 vaccinations and autoimmune disorders. Moreover, it will explore the evidence informing the consensus of care concerning COVID-19 vaccinations in people experiencing these disorders. Data from refereed articles and preprints will be synthesized through a thematic analysis. A subgroup analysis will compare the findings according to the previous existence of autoimmune disorders, presence of co-morbidities, vaccine type, and other potentially relevant factors. COVID-19 has triggered the largest vaccination campaign in history. Drug safety is critical to properly assess the balance of risks and benefits of any medical intervention. Our investigation should yield information useful to assist in clinical decision-making, policy development, and ethical medical practices.
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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 | 0.075 | 0.072 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.011 | 0.010 |
| Bibliometrics | 0.022 | 0.016 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.066 | 0.010 |
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".