Predictors of Breakthrough SARS-CoV-2 Infection after Vaccination
Bibliographic record
Abstract
The initial two-dose vaccine series and subsequent booster vaccine doses have been effective in modulating SARS-CoV-2 disease severity and death but do not completely prevent infection. The correlates of infection despite vaccination continue to be under investigation. In this prospective decentralized study (n = 1286) comparing antibody responses in an older- (≥70 years) to a younger-aged cohort (aged 30-50 years), we explored the correlates of breakthrough infection in 983 eligible subjects. Participants self-reported data on initial vaccine series, subsequent booster doses and COVID-19 infections in an online portal and provided self-collected dried blood spots for antibody testing by ELISA. Multivariable survival analysis explored the correlates of breakthrough infection. An association between higher antibody levels and protection from breakthrough infection observed during the Delta and Omicron BA.1/2 waves of infection no longer existed during the Omicron BA.4/5 wave. The older-aged cohort was less likely to have a breakthrough infection at all time-points. Receipt of an original/Omicron vaccine and the presence of hybrid immunity were associated with protection of infection during the later Omicron BA.4/5 and XBB waves. We were unable to determine a threshold antibody to define protection from infection or to guide vaccine booster schedules.
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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 | 0.002 |
| 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) | 0.002 | 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".