Serologic Response to Vaccine for COVID-19 in Patients with Hematologic Malignancy: A Prospective Cohort Study
Bibliographic record
Abstract
BACKGROUND: Patients with hematological cancers have increased COVID-19 morbidity and mortality, and these patients show attenuated vaccine responses. This study aimed to characterize the longitudinal humoral immune responses to COVID-19 vaccination in patients with hematological malignancies. PATIENTS AND METHODS: We conducted a prospective cohort study, collecting samples from March 2021 to July 2022, from patients seen at a cancer treatment center in London, Ontario, Canada, who met the following eligibility criteria: age ≥18 years, diagnosed with a hematological malignancy, recipient of a COVID-19 vaccine during the study period, and able to provide informed consent. RESULTS: Median anti-S titers (MST) were 0.0, 64.0, and 680.5 U/mL following first (V1), second (V2), and third (V3) vaccine doses, respectively. Patients with lymphoid malignancies' response to vaccination was attenuated compared to myeloid malignancy patients after V2 and V3 (P < .001, P < .01). Active treatment was associated with lower antibody titers (MST 10) compared to treatment 12-24 months (MST 465, P = .04367) and >24 months (MST 1660.5, P = .0025) prior to vaccination. V3 significantly increased antibody titers compared to V2 for patients less than 3 months from treatment. Increasing age was associated with smaller antibody response following V2 (P < .05), but not following V3. Patients receiving anti-CD20 therapy did not demonstrate increased antibody titer levels after V3 (V2 MST 0, V3 MST 0; P > .05). CONCLUSION: We report an attenuated serologic response to COVID-19 vaccination in our study population of patients with hematological malignancy. The immune response to vaccination was affected by patient age, diagnosis, treatment, and timing of treatment exposure.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".