Comparison of three dosing intervals for the primary vaccination of the SARS-CoV-2 mRNA Vaccine (BNT162b2) on magnitude, neutralization capacity and durability of the humoral immune response in health care workers: A prospective cohort study
Bibliographic record
Abstract
OBJECTIVES: The dosing interval of a primary vaccination series can significantly impact on vaccine immunogenicity and efficacy. The current study compared 3 dosing intervals for the primary vaccination series of the BNT162b2 mRNA COVID-19 vaccine, on humoral immune response and durability against SARS-CoV-2 ancestral and Beta variants up to 9 months post immunization. METHODS: Three groups of age- and sex-matched healthcare workers (HCW) who received 2 primary doses of BNT162b2 separated by 35-days, 35-42 days or >42-days were enrolled. Vaccine induced antibody titers at 3 weeks, 3 and 6-9 months post-second dose were assessed. RESULTS: There were 309 age- and sex-matched HCW (mean age 43 [sd 13], 58% females) enrolled. Anti-SARS-CoV-2 binding (IgG, IgM, IgA) and neutralizing antibody titers showed significant waning in levels beyond 35 days post first dose. The second dose induced a significant rise in antibody titers, which peaked at 3 weeks and then declined at variable rates across groups. The magnitude, consistency and durability of response was greater for anti-Spike than anti-RBD antibodies; and for IgG than IgA or IgM. Compared to the shorter schedules, a longer interval of >42 days offered the highest binding and neutralizing antibody titers against SARS-CoV-2 ancestral and Beta (B1.351) variants beyond 3 months post-vaccination. CONCLUSIONS: This is the first comprehensive study to compare 3 dosing intervals for the primary vaccination of BNT162b2 mRNA COVID-19 vaccine implemented in the real world. These findings suggest that delaying the second dose beyond 42 days can potentiate and prolong the humoral response against ancestral and Beta variants of SARS-CoV-2 up to 9 months post-vaccination.
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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 | 0.001 |
| 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 | 0.001 | 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".