The effect of rituximab on vaccine responses in patients with immune thrombocytopenia
Bibliographic record
Abstract
B-cell depletion may impair vaccine responses and increase infection risk in patients with immune thrombocytopenia (ITP). We investigated the effects of rituximab on antibody and cellular responses to Streptococcus pneumoniae polysaccharide and Haemophilus influenzae type b (Hib) vaccines in ITP patients. Of 60 patients in the main trial, 24 patients received both vaccines 6 months after rituximab (n = 17) or placebo (n = 7). Among 20 evaluable patients, 3 of 14 (21%) in the rituximab group and 4 of 6 (67%) in the placebo group achieved a fourfold increase in anti-pneumococcal antibodies (P = .12). For anti-Hib antibodies, 4 of 14 (29%) and 5 of 6 (83%), respectively, achieved a fourfold increase (P < .05). Fewer patients in the rituximab group demonstrated Hib killing (2 of 14 [14%], 5 of 6 [83%], P < .05). Three of 14 rituximab-treated patients failed to respond to vaccines by any criteria. After vaccinations, preplasma cell blasts and interferon-γ-secreting T cells were reduced in rituximab-treated patients. Antibody responses were impaired for at least 6 months after rituximab. Cellular immunity was reduced in parallel with depleted B-cell pools. These findings have implications for the timing of vaccinations and the mechanism of infection after rituximab in ITP patients.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.000 | 0.001 |
| 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".