Quantitative Analysis of SARS-CoV-2 Antibody Status between Patients with Cancer and Healthy Individuals with Extended Vaccination Dosing Intervals in Canada
Bibliographic record
Abstract
(1) Background: To date, data addressing the antibody response of cancer patients to SARS-CoV-2 vaccines are limited. To our knowledge, this is the first report to evaluate humoral immunity. responses in Canadian cancer patients. (2) Methods: 116 cancer patients and 35 healthy participants were enrolled in this cross-sectional study. The interval between the first and second doses were closely matched during analysis. IgG antibodies against the SARS-CoV-2 spike receptor–binding domain were determined using an enzyme-linked immunosorbent assay (ELISA). (3) Results: Following two doses of SARS-CoV-2 vaccine (including BNT162b2, AZD1222, and mRNA-1273), the mean serum anti-spike protein antibody level was 382.4 BAU/mL (binding antibody unit, SD ± 9.4) in the control group, 265.8 BAU/mL (±145.7) in solid cancer patients, and 168.2 BAU/mL (±172.9) in hematological cancer patients. Observed differences were significantly lower in both solid and hematological groups when comparing to the control group (p ≤ 0.0001). In solid cancer group, patients with cytotoxic chemotherapy demonstrated significantly lower antibody levels (p < 0.01), whereas the rest of the patients showed similar antibody levels as the healthy control. Antibody levels were lower in those on treatment than those off treatment in patients with hematological malignancies (p < 0.0001) but not for those with solid cancers (p = 0.4553). (4) Conclusions: After two doses of the SARS-CoV-2 vaccination, patients with solid and hematological malignancies demonstrated impaired serological responses. This was particularly prominent if there was cytotoxic chemotherapy or systemic therapy in solid and hematological cancer, respectively.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".