The price of performance: a cost and performance analysis of the implementation of cell‐free fetal DNA testing for Down syndrome in Ontario, Canada
Bibliographic record
Abstract
OBJECTIVE: To examine the cost and performance implications of introducing cell-free fetal DNA (cffDNA) testing within modeled scenarios in a publicly funded Canadian provincial Down syndrome (DS) prenatal screening program. METHOD: Two clinical algorithms were created: the first to represent the current screening program and the second to represent one that incorporates cffDNA testing. From these algorithms, eight distinct scenarios were modeled to examine: (1) the current program (no cffDNA), (2) the current program with first trimester screening (FTS) as the nuchal translucency-based primary screen (no cffDNA), (3) a program substituting current screening with primary cffDNA, (4) contingent cffDNA with current FTS performance, (5) contingent cffDNA at a fixed price to result in overall cost neutrality,(6) contingent cffDNA with an improved detection rate (DR) of FTS, (7) contingent cffDNA with higher uptake of FTS, and (8) contingent cffDNA with optimized FTS (higher uptake and improved DR). RESULTS: This modeling study demonstrates that introducing contingent cffDNA testing improves performance by increasing the number of cases of DS detected prenatally, and reducing the number of amniocenteses performed and concomitant iatrogenic pregnancy loss of pregnancies not affected by DS. Costs are modestly increased, although the cost per case of DS detected is decreased with contingent cffDNA testing. CONCLUSION: Contingent models of cffDNA testing can improve overall screening performance while maintaining the provision of an 11- to 13-week scan. Costs are modestly increased, but cost per prenatally detected case of DS is decreased.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".