A systematic literature review of the economic and healthcare resource burden of cytomegalovirus
Bibliographic record
Abstract
Cytomegalovirus (CMV) can infect individuals at any age, including infants, who may contract it from infected mothers (congenital CMV [cCMV]). Whereas CMV infection is typically asymptomatic or causes mild illness in healthy individuals, infection can result in severe outcomes in immunocompromised individuals and in infants with cCMV. This systematic review aims to characterize the economic impact of CMV and cCMV infections. Medline, Embase, and LILACS databases were searched for publications reporting the economic impact of cCMV and CMV infections across all age groups. Manuscripts published between 2010 and 2020 from Australia, Latin America, Canada, Europe, Israel, Japan, the United States, and global (international, worldwide) studies were included; congress materials were excluded. Outcomes of interest included cCMV- and CMV-attributable direct costs/charges, resource utilization, and indirect/societal costs. Of 751 records identified, 518 were excluded based on duplication, population, outcome, study design, or country. Overall, 55 articles were eligible for full-text review; 25 were further excluded due to population, outcome, study design, or congress abstract. Two publications were additionally identified, resulting in economic impact data compiled from 32 publications. Of these, 24 publications reported cost studies of cCMV or CMV, including evaluation of direct costs/charges, healthcare resource utilization, and indirect/societal costs, and 7 publications reported economic evaluations of interventions. The populations, methods and outcomes used across these studies varied widely. CMV and cCMV infections impose a considerable economic impact on different countries, populations, and outcomes. There are substantial evidence gaps where further research is warranted. A plain language summary of this manuscript can be found here.
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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 | 0.010 | 0.055 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.016 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".