The cost‐effectiveness of maternal and neonatal screening for congenital cytomegalovirus infection in Japan
Bibliographic record
Abstract
Congenital cytomegalovirus infection is the most common congenital infection. Using a decision tree model, cost-effectiveness of maternal screening with subsequent prenatal valacyclovir treatment and newborn screening with neonatal valganciclovir treatment was evaluated. The incremental cost-effectiveness ratio (ICER) was calculated for (1) universal maternal antibody screening with prenatal valacyclovir treatment compared to targeted newborn screening, and (2) universal newborn screening with postnatal valganciclovir treatment compared to targeted newborn screening. We performed a one-way sensitivity analysis. Compared to targeted newborn screening, the ICERs for universal newborn screening and maternal screening were 2 966 296 Japanese Yen (JPY) (21 188 USD) and 1 026 984 JPY (7336 USD), respectively. In all scenarios in the one-way sensitivity analysis, the ICERs of the maternal screening and the universal newborn screening strategies were less than three gross domestic product per capita compared with the targeted newborn screening strategy. Both maternal and universal newborn screening strategies may be cost-effective than a targeted newborn screening program. The potential utility of the maternal screening with valacyclovir treatment strategy, while potentially cost effective in regions with lower baseline seroprevalence rates, requires further study as the modeling was based on limited evidence.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".