Projected cost-savings with herpes simplex virus screening in pregnancy: towards a new screening paradigm
Bibliographic record
Abstract
OBJECTIVES: Herpes simplex virus (HSV) infections in newborns are an uncommon but potentially devastating consequence of genital HSV infection in women. Current practice focuses on preventing perinatal transmission by women with prevalent HSV, but transmission risk is greatest when genital HSV is acquired for the first time late in pregnancy. The objective of this study was to assess the effectiveness and cost effectiveness of identifying pregnant women at risk of de novo HSV acquisition as a means of preventing vertical HSV transmission. METHODS: A Bayesian decision tree model was parameterized using the best available health and economic data relating to HSV in pregnancy and was used to evaluate the cost effectiveness of screening to identify individuals susceptible to HSV infection in a hypothetical cohort of 100,000 pregnant women in their second trimester of pregnancy. Final outcomes were the projected incidence of maternal and neonatal HSV, quality-adjusted life expectancy and life-time costs associated with neonatal HSV. RESULTS: In the absence of testing, model projected incidence of neonatal HSV was 34 cases per 100,000 births, similar to available surveillance data. Screening pregnant women and their partners was projected to decrease the incidence of HSV-1 and HSV-2 infections in women and infants and to save costs. These findings were robust under alternative assumptions and in wide-ranging sensitivity analyses. CONCLUSIONS: The use of accurate and relatively inexpensive serological tests for HSV to identify women vulnerable to incident HSV infection in pregnancy has the potential to reduce neonatal HSV incidence and reduce health-related costs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".