Modestly protective cytomegalovirus vaccination of young children effectively prevents congenital infection at the population level
Bibliographic record
Abstract
Abstract A vaccine to prevent congenital cytomegalovirus infection (cCMV) is a public health priority. cCMV results from maternal primary or non-primary CMV infection (reinfection or reactivation of chronic infection) during pregnancy. Young children are a major source of transmission to pregnant women because they shed CMV at high viral loads for prolonged periods. CMV vaccines evaluated in clinical trials so far have demonstrated only approximately 50% efficacy against maternal primary infection. None of these have been approved, as higher levels of vaccine-induced immunity are assumed to be required to substantially reduce cCMV prevalence. Here, we designed a mathematical model to capture the relationship between viral shedding by young children and maternal CMV infections during pregnancy. Using this model, we were able to quantify the efficacy of CMV immunity following infection to protect against reinfection and viral shedding. There was a 35% reduction in the risk of infection to a seropositive person (reinfection) versus a seronegative person (primary infection), given the same exposure. Viral shedding following reinfection was only 25% the quantity of that following primary infection. We also found that a vaccine that confers the equivalent of infection-induced immunity, when given to young children, markedly reduces both CMV transmission to pregnant women and rates of cCMV. Thus, we predict that vaccine candidates that have already been shown to be only modestly protective may in fact be highly effective at preventing cCMV by interrupting child-to-mother transmission.
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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".