Vaccine value profile for cytomegalovirus
Bibliographic record
Abstract
Cytomegalovirus (CMV) is the most common infectious cause of congenital malformation and a leading cause of developmental disabilities such as sensorineural hearing loss (SNHL), motor and cognitive deficits. The significant disease burden from congenital CMV infection (cCMV) led the US National Institute of Medicine to rank CMV vaccine development as the highest priority. An average of 6.7/1000 live births are affected by cCMV, but the prevalence varies across and within countries. In contrast to other congenital infections such as rubella and toxoplasmosis, the prevalence of cCMV increases with CMV seroprevalence rates in the population. The true global burden of cCMV disease is likely underestimated because most infected infants (85-90 %) have asymptomatic infection and are not identified. However, about 7-11 % of those with asymptomatic infection will develop SNHL throughout early childhood. Although no licensed CMV vaccine exists, several candidate vaccines are in development, including one currently in phase 3 trials. Licensure of one or more vaccine candidates is feasible within the next five years. Various models of CMV vaccine strategies employing different target populations have shown to provide substantial benefit in reducing cCMV. Although CMV can cause end-organ disease with significant morbidity and mortality in immunocompromised individuals, the focus of this vaccine value profile (VVP) is on preventing or reducing the cCMV disease burden. This CMV VVP provides a high-level, comprehensive assessment of the currently available data to inform the potential public health, economic, and societal value of CMV vaccines. The CMV VVP was developed by a working group of subject matter experts from academia, public health groups, policy organizations, and non-profit organizations. All contributors have extensive expertise on various elements of the CMV VVP and have described the state of knowledge and identified the current gaps. The VVP was developed using only existing and publicly available information.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".