A Cost-Effectiveness Analysis of Prenatal Screening Strategies for Down Syndrome
Why this work is in the frame
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Bibliographic record
Abstract
OBJECTIVE: To evaluate which Down syndrome screening strategy is the most cost-effective. METHODS: Using decision-analysis modeling, we compared the cost-effectiveness of 9 screening strategies for Down syndrome: 1) no screening, 2) first-trimester nuchal translucency (NT) only, 3) first-trimester combined NT and serum screen, 4) first-trimester serum only, 5) quadruple screen, 6) integrated screening, 7) sequential screening, 8) integrated serum only, or 9) maternal age. Costs included cost of tests and resources used for raising a child with Down syndrome. One-way and multiway sensitivity analyses were performed for all model variables. The main outcome measures were cost per Down syndrome case detected, rate of delivering a liveborn neonate with Down syndrome, and rate of diagnostic procedure-related pregnancy loss for each strategy. RESULTS: Sequential screening detected more Down syndrome cases compared with the other strategies, but it had a higher procedure-related loss rate. Integrated serum screening was the most cost-effective strategy. Sensitivity analyses revealed the model to be robust over a wide range of values for the variables. The addition of the cost of genetic sonogram to the second-trimester strategies resulted in first-trimester combined screening becoming the most cost-effective strategy. CONCLUSION: Within our baseline assumptions, integrated serum screening was the most cost-effective screening strategy for Down syndrome. If the cost of nuchal translucency is less than dollars 57 or when genetic sonogram is included in the second-trimester strategies, first-trimester combined screening became the most cost-effective strategy. LEVEL OF EVIDENCE: III.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 it