The Third Dose Is the Charm: Effective Cellular and Humoral Immune Responses to Third COVID-19 Vaccine Doses in Immunosuppressed Nonresponders
Bibliographic record
Abstract
Pathogens drive an effective immune response by stimulating the innate immune system, leading to activation of host T and B cells.1 Subsequent pathogen exposure leads to a more robust response through memory T and B cells. Vaccines attempt to mimic the host-pathogen response to provide long-lasting cellular and humoral protection without the sequelae of disease.2,3 Patients with autoimmune diseases or solid organ transplant recipients receive immunosuppressive medications that limit productive immune responses following vaccination. Indeed, multiple case series show that the humoral response is blunted following the 2-dose SARS-CoV-2 mRNA primary vaccine series while taking mycophenolate, rituximab, or methotrexate (MTX).4-7 In this issue of The Journal of Rheumatology , Isnardi and colleagues evaluate the cellular and humoral responses after a third SARS-CoV-2 vaccine dose in patients with rheumatoid arthritis (RA) who did not develop detectable anti-SARS-CoV-2 antibodies following completion of the primary SARS-CoV-2 vaccine series.8 Isnardi et al8 enrolled 21 patients who met 2010 American College of Rheumatology (ACR)/European Alliance of Associations for Rheumatology criteria … Address correspondence to Dr. M.C. Baker, Assistant Professor of Medicine, Clinical Chief, Division of Immunology and Rheumatology, Stanford University, 1000 Welch Road, Suite 203, Palo Alto, CA 94304, USA. Email: mbake13{at}stanford.edu.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".