Vaccine response following anti-CD20 therapy: a systematic review and meta-analysis of 905 patients
Bibliographic record
Abstract
The objective of this study was to perform a systematic review of the literature on vaccine responsiveness in patients who have received anti-CD20 therapy. PubMed and EMBASE were searched up to 4 January 2021 to identify studies of vaccine immunogenicity in patients treated with anti-CD20 therapy, including patients with hematologic malignancy or autoimmune disease. The primary outcomes were seroprotection (SP), seroconversion (SC), and/or seroresponse rates for each type of vaccine reported. As the pandemic influenza vaccine (2009 H1N1) has standardized definitions for SP and SC, and represented a novel primary antigen similar to the COVID-19 vaccine, meta-analysis was conducted for SC of studies of this vaccine. Pooled estimates, relative benefit ratios (RBs), and 95% confidence intervals (CIs) were calculated using a random-effects model. Thirty-eight studies (905 patients treated with anti-CD20 therapy) were included (19 studies of patients with hematologic malignancies). Patients on active (<3 months since last dose) anti-CD20 therapy had poor responses to all types of vaccines. The pooled estimate for SC after 1 pandemic influenza vaccine dose in these patients was 3% (95% CI, 0% to 9%), with an RB of 0.05 (95% CI, 0-0.73) compared with healthy controls and 0.22 (95% CI, 0.09-0.56) compared with disease controls. SC compared with controls seems abrogated for at least 6 months following treatment (3-6 months post anti-CD20 therapy with an RB of 0.50 [95% CI, 0.24-1.06] compared with healthy and of 0.44 [95% CI, 0.23-0.84] compared with disease controls). For all vaccine types, response to vaccination improves incrementally over time, but may not reach the level of healthy controls even 12 months after therapy.
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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 | 0.012 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.029 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".