Antibody Responses 3-5 Months Post-Vaccination with mRNA-1273 or BNT163b2 in Nursing Home Residents
Bibliographic record
Abstract
Abstract Nursing home residents often fail to mount robust responses to vaccinations and recent reports of breakthrough infections, particularly from variants of concern, raise questions about whether vaccination regimens elicit a sufficient humoral immune response or if booster doses are warranted. We examined SARS-CoV-2 antibody levels and neutralizing capacity in nursing home residents 3-5 months after 2 doses of mRNA-1273 or BNT163b2 vaccination as per recommended schedules. Nursing home residents were recruited from eight long-term care homes in Ontario, Canada, between March and July 2021. Antibody levels and neutralization capacity from a previously published convalescent cohort were used as a comparator. Serum SARS-CoV-2 IgA/G/M against spike (S) protein and its receptor-binding domain (RBD) were measured by validated ELISA, with assay cut-off at the mean and 3 standard deviations of a pre-COVID-19 population from the same geographic region. Antibody neutralization was measured against the wild-type strain of SARS-CoV-2 and the beta variant of concern (B.1.351). No neutralizing antibodies were detected in ∼20% of residents to the wild-type virus (30/155; 19%) or beta variant (27/134; 20%). Residents that received BNT163b2 had a ∼4-fold reduction in neutralization to the wild-type strain, and a ∼2-fold reduction in neutralization to the beta variant relative to those who received mRNA-1273. Current mRNA SARS-CoV-2 vaccine regimens may not have equivalent efficacy in nursing home residents. Our findings imply that differences in the humoral immune response may contribute to breakthrough infections, and suggest that consideration of the type of vaccine administered to older adults will have a positive impact on the generation of protective immunity.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".