Prenatal Screening for CMV Primary Infection : A Cost-Utility Model
Bibliographic record
Abstract
<b>Objective: </b>Congenital cytomegalovirus (CMV) infection is a major cause of deafness and neurodevelopmental disability in children. Our objective was to assess the cost utility of first-trimester serological CMV screening, compared to screening of high-risk pregnancies and no serological screening. <b>Design: </b>A decision-analytic model was created to compare the cost utility of three strategies from a healthcare sector perspective: universal first-trimester serological screening, screening only of high-risk pregnant women (both including antiviral prophylaxis in cases of primary infection) and serological testing triggered by foetal morphological ultrasound (no CMV serological screening). <b>Setting: </b>Canada. <b>Population: </b>Hypothetical population of 80 000 pregnant women. <b>Methods: </b>Probability, expected values and cost estimates were derived from published literature and local hospital and national insurance data. <b>Main outcome measure: </b>Cost per maternal and infant quality-adjusted life year (QALY) lost. <b>Results: </b>Universal serological screening was superior to both screening of high-risk women and no screening (utility of -0.42, -0.63 and - 0.87 QALY lost, respectively). Sensitivity analysis demonstrated that universal screening was the most cost-effective strategy regardless of the incidence of primary infection, the acceptability of amniocentesis and the efficacy of antiviral prophylaxis. In the Monte Carlo analyses, universal serological screening was the most cost-effective option in 96.36% of simulations. Universal serological screening would allow detection of 152 cases of primary maternal CMV infection and would prevent 29 cases of congenital CMV infection annually. <b>Conclusion: </b>Our findings support the adoption of a population-based prenatal screening programme for the prevention of congenital CMV infection.
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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) | 0.000 | 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) | 0.000 | 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".