Improved antibody breadth with an extended primary dose interval of COVID-19 vaccine is overcome by boosters
Bibliographic record
Abstract
Introduction: During rollout of mRNA-based COVID-19 vaccines, several jurisdictions extended the interval between the first and second doses to prioritize wider population access to limited vaccine supply. This study evaluated the effects of an extended dose interval on development of antibody and cell-mediated responses following the primary dose series and a subsequent booster dose. Methods: Blood samples were collected from mRNA COVID-19 vaccine recipients at baseline and longitudinally after each dose. Samples were analyzed for SARS-CoV-2-specific antibody titers, neutralizing antibodies and memory T cell responses. Results: An extended dose interval was associated with improved breadth of neutralizing antibody responses against both ancestral and early SARS-CoV-2 variants, but not Omicron variants. Dose interval had no impact on the development of antigen-specific memory T cell responses, the memory or T helper phenotypes of responding T cells or cytokine production. The effects of the primary dose interval on immune outcomes were no longer evident after a third dose of mRNA vaccine. Discussion: An extended primary dose interval resulted in short-term benefits to humoral immunity but these were transient in the context of subsequent exposures. However, in addition to the public health benefits of wider population access to vaccines, the short-term immunological benefits of extending the dose interval may have been sustained in the absence of boosters. These findings underscore the importance of evaluating dosing intervals during the development of future vaccine candidates.
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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 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".