How Safe Are COVID-19 Vaccines in Individuals with Immune-Mediated Inflammatory Diseases? The SUCCEED Study
Bibliographic record
Abstract
We were tasked by Canada's COVID-19 Immunity Task Force to describe severe adverse events (SAEs) associated with emergency department (ED) visits and/or hospitalizations in individuals with immune-mediated inflammatory diseases (IMIDs). At eight Canadian centres, data were collected from adults with rheumatoid arthritis (RA), axial spondyloarthritis (AxS), systemic lupus (SLE), psoriatic arthritis (PsA), and inflammatory bowel disease (IBD). We administered questionnaires, analyzing SAEs experienced within 31 days following SARS-CoV-2 vaccination. About two-thirds (63%) of 1556 participants were female; the mean age was 52.5 years. The BNT162b2 (Pfizer) vaccine was the most common, with mRNA-1273 (Moderna) being second. A total of 49% of participants had IBD, 27.4% had RA, 14.3% had PsA, 5.3% had SpA, and 4% had SLE. Twelve (0.77% of 1556 participants) SAEs leading to an ED visit or hospitalization were self-reported, occurring in 11 participants. SAEs included six (0.39% of 1556 participants) ED visits (including one due to Bell's Palsy 31 days after first vaccination) and six (0.39% of 1556 participants) hospitalizations (including one due to Guillain-Barré syndrome 15 days after the first vaccination). Two SAEs included pericarditis, one involved SLE (considered a serious disease flare), and one involved RA. Thus, in the 31 days after SARS-CoV-2 vaccination in our IMID sample, very few serious adverse events occurred. As SARS-CoV2 continues to be a common cause of death, our findings may help optimize vaccination acceptance.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".