Immunogenicity of a Three-Dose Primary Series of mRNA COVID-19 Vaccines in Patients With Lymphoid Malignancies
Bibliographic record
Abstract
Abstract Background Patients with lymphoid malignancies are at risk for poor coronavirus disease 2019 (COVID-19)-related outcomes and have reduced vaccine-induced immune responses. Currently, a 3-dose primary regimen of mRNA vaccines is recommended in the United States for immunocompromised hosts. Methods A prospective cohort study of healthy adults (n = 27) and patients with lymphoid malignancies (n = 94) was conducted, with longitudinal follow-up through completion of a 2- or 3-dose primary mRNA COVID vaccine series, respectively. Humoral responses were assessed in all participants, and cellular immunity was assessed in a subset of participants. Results The rate of seroconversion (68.1% vs 100%) and the magnitude of peak anti-S immunoglobulin G (IgG) titer (median anti-S IgG = 32.4, IQR = 0.48–75.0 vs median anti-S IgG = 72.6, IQR 51.1–100.1; P = .0202) were both significantly lower in patients with lymphoid malignancies compared to the healthy cohort. However, peak titers of patients with lymphoid malignancies who responded to vaccination were similar to healthy cohort titers (median anti-S IgG = 64.3; IQR, 23.7–161.5; P = .7424). The third dose seroconverted 7 of 41 (17.1%) patients who were seronegative after the first 2 doses. Although most patients with lymphoid malignancies produced vaccine-induced T-cell responses in the subset studied, B-cell frequencies were low with minimal memory cell formation. Conclusions A 3-dose primary mRNA series enhanced anti-S IgG responses to titers equivalent to healthy adults in patients with lymphoid malignancies who were seropositive after the first 2 doses and seroconverted 17.1% who were seronegative after the first 2 doses. T-cell responses were present, raising the possibility that the vaccines may confer some cell-based protection even if not measurable by anti-S IgG.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 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".