Antibody Response to COVID-19 mRNA Vaccines in Oncologic and Hematologic Patients Undergoing Chemotherapy
Bibliographic record
Abstract
BACKGROUND: Information on immune responses in cancer patients following mRNA COVID-19 vaccines is still insufficient, but generally, patients had impaired serological responses, especially those with hematological malignancies. We evaluated serological response to COVID-19 mRNA vaccine in cancer patients receiving chemotherapy compared with healthy controls. METHODS: In total, 195 cancer patients and 400 randomly selected controls who had been administered a Pfizer-BioNTech or Moderna COVID-19 vaccines in two doses were compared. The threshold of positivity was 4.33 BAU/mL. Patients were receiving anticancer treatment after the first and second dose of the vaccines. RESULTS: a TOTAL OF 169 patients (87%) had solid tumors and 26 hemolymphopoietic diseases. Seropositivity rate was lower in patients than controls (91% vs. 96%), with an age/gender-adjusted rate ratio (RR) of 0.95 (95% CL = 0.89-1.02). Positivity was found in 97% of solid cancers and in 50% of hemolymphopoietic tumors. Both advanced and adjuvant therapy seemed to slightly reduce seropositivity rates in patients when compared to controls (RR = 0.97, 95% CL = 0.89-1.06; RR = 0.94, 95% CL = 0.87-1.01). CONCLUSIONS: the response to vaccination is similar in patients affected by solid tumors to controls. On the contrary, hemolymphopietic patients show a much lower response than controls.
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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 | 0.000 | 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".