Additional Reassuring Data for SARS-CoV-2 Vaccination in Immune-Mediated Inflammatory Diseases, but With a Catch
Bibliographic record
Abstract
Vaccination remains the most important intervention to attenuate the risk and severity of infectious diseases in those immunosuppressed for the treatment of rheumatic conditions and is indeed true for coronavirus disease 2019 (COVID-19). Substantial reductions in the hazard ratios (HRs) of breakthrough infections were observed in those with immune-mediated inflammatory diseases (IMIDs) vaccinated with the initial series (HR 0.143) and after the third dose (HR 0.017) of BNT162b2 compared to unvaccinated patients.1 In addition, analyses from the COVID-19 Global Rheumatology Alliance revealed that risk factors associated with severe COVID-19 or death due to COVID-19 during the initial (common or D614G variant) epoch of COVID-19 (ie, B cell–depleting therapies, glucocorticoids [GCs], sulfasalazine use) were still observed as risk factors during the Omicron epoch in unvaccinated patients with IMIDs.2 As COVID-19 continues to disproportionately affect those on immunosuppressive therapies for the treatment of IMIDs, a complete understanding of the effect that immunosuppression has on vaccination against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is needed. It was recognized early on that several classes of immunosuppressive therapies exerted more detrimental effects on the humoral immune responses to SARS-CoV-2 vaccination than others. Consistent reductions in total antispike IgG and neutralization titers were observed in those on B cell–depleting agents, mycophenolate, GCs, and Janus kinase (JAK) inhibitors following the initial series of mRNA SARS-CoV-2 vaccines (reviewed in Grainger et al3). Despite these important early data, there were several mechanisms of action that remained unreported or underpowered, such as IL-17 inhibition (IL-17i). In addition, many of these early reports tended to treat all IMIDs as one, not accounting for any confounding by disease state itself, such as the influence of tumor necrosis factor (TNF) inhibitors (TNFi) in rheumatoid arthritis (RA), spondyloarthritis (SpA), or inflammatory bowel disease. In this issue of The Journal of … Address correspondence to Dr. A.H.J. Kim, Washington University School of Medicine, Rheumatology, MSC 8045-0020-10, 660 S Euclid Ave, St. Louis, MO 63110, USA. Email: akim{at}wustl.edu.
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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 | 0.017 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.010 |
| Insufficient payload (model declined to judge) | 0.044 | 0.008 |
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".