Trends in the use of prenatal testing services for fetal aneuploidy in Ontario: a descriptive study
Bibliographic record
Abstract
Background: In 2014, Ontario augmented its publicly funded multiple-marker screening program for prenatal aneuploidy by incorporating cell-free fetal DNA (cffDNA) analysis for high-risk pregnancies. We assessed trends in the use of multiple-marker screening, cffDNA screening and prenatal diagnostic testing before and after implementation of public funding. Methods: We conducted a descriptive study based on data from the Better Outcomes Registry & Network (BORN) Ontario. The study population included all pregnant women in Ontario with a singleton pregnancy and an expected date of delivery between July 1, 2012, and Mar. 31, 2016, with pregnancy data captured in BORN. Pregnancy losses and terminations before 20 weeks’ gestation not captured in BORN were excluded. We generated descriptive statistics to show trends and regional variations in use. Results: The study sample included 534 210 singleton pregnancies. After cffDNA screening was funded for specific indications, uptake of multiple-marker screening increased slightly, from 66.5% to 68.1% (p < 0.001). Uptake of cffDNA screening among women with a positive multiple-marker screening result increased substantially, from 3.2% to 48.8% (p < 0.001). In contrast, the rate of prenatal diagnostic testing in this group decreased from 54.8% to 30.8% (p < 0.001). Although women aged 40 years or older are eligible for primary cffDNA screening, only a small decrease in the use of multiple-marker screening was observed in this group. The greatest use of cffDNA screening and greatest decline in prenatal diagnostic testing were seen in women with a level of risk for trisomy 21 of 1:101–1:200 based on multiple-marker screening. Interpretation: After public funding of cffDNA screening was implemented in Ontario, there was a significant increase in cffDNA screening and a significant decrease in prenatal diagnostic testing among women with a positive multiple-marker screening result. These changing patterns show the significant impact of public policy and funding decisions on women’s choices regarding prenatal testing.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".