B Cell Reconstitution is Associated With COVID-19 Booster Vaccine Responsiveness in Patients Previously Seronegative Treated With Rituximab
Bibliographic record
Abstract
Objective To assess factors associated with serologic response to the coronavirus 2019 (COVID-19) booster vaccine in patients with autoimmune rheumatic diseases treated with rituximab (RTX) who were previously serologically unresponsive to the initial vaccine series. Methods A retrospective chart review of patients treated with RTX who failed to demonstrate a serologic response to the first SARS-CoV-2 vaccination series and subsequently received an mRNA vaccine booster was performed. Serologic response ≥ 4 weeks after the booster was the primary outcome. Fisher exact tests,ttests, and Wilcoxon rank-sum tests were used for comparisons. Results In 31 patients who were previously seronegative, 68% seroconverted following a booster of the COVID-19 vaccine. B cell reconstitution was significantly different between those with positive (median 1.79, IQR 0.65-3.00) and negative (median 0, IQR 0-0) serologic responses to the booster. The days from last RTX dose were also statistically different among seroconverters (median 301, IQR 251-368) vs nonseroconverters (median 188, IQR 169-245). Demographic characteristics were not associated with serologic positivity. Positive predictive value of B cell presence was 90.9% (95% CI 70.8-98.9) and negative predictive value was 100% (95% CI 59-100) for serologic response to the mRNA booster vaccine. Positive predictive value of time ≥ 6 months from last RTX dose to booster was 78.3% (95% CI 56.3-92.5) and the negative predictive value was 62.5% (95% CI 24.5-91.5). Conclusion Detectable B cells and longer time from last RTX exposure were associated with the development of anti-SARS-CoV-2 spike protein antibodies following the booster vaccine. These findings should be considered in timing boosters in patients treated with RTX.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".