The effect of funding non-invasive prenatal testing (NIPT) on invasive procedures performed to identify trisomy 21 pregnancies: A population-based cohort study
Bibliographic record
Abstract
Background: Screening for Trisomy 21 in Nova Scotia has traditionally included serum integrated prenatal screening (SIPS, maternal serum screening), and integrated prenatal screening (maternal serum screening with nuchal translucency, IPS) for those patients considered to be at high risk. In 2016, non-invasive prenatal testing (NIPT) became available as a funded second tier screen for pregnancies at high risk for Trisomy 21 in Nova Scotia.Objective: To compare pregnancy characteristics and number of diagnostic procedures performed for high risk of Trisomy 21 before and after introduction of funded NIPT in Nova Scotia.Methods: This population-based retrospective cohort study evaluated pregnancies with diagnostic testing and/or NIPT which were identified through the IWK Health Clinical Genomics Laboratory Database. Maternal chart review was performed for each pregnancy to confirm eligibility and collect demographic data. Descriptive statistics comparing number of diagnostic procedures and pregnancy characteristics were performed among two epochs – pre-NIPT (2012-2015) and post-NIPT (2016-2019) using Fisher’s exact test, and rates of Trisomy 21 confirmed by diagnostic testing between the two epochs were described.Results: The population incidence of Trisomy 21 remained stable and maternal demographics were similar between the two epochs; after the introduction of funded NIPT, the number of diagnostic procedures decreased, and when diagnostic testing was performed, the procedures were 6-fold more likely to confirm Trisomy 21 (95% CI 2.6-12.9) following high risk screening.Conclusion: The decrease in diagnostic procedures with an increase in the prenatal detection of Trisomy 21 demonstrated in this study illustrates the value of NIPT in a population with limited resources for first trimester screening.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".