A Cost-Effectiveness Analysis of Prenatal Screening Strategies for Down Syndrome
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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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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".