The impact of maternal and geographical factors on the uptake of non‐invasive prenatal testing: A retrospective cohort study
Bibliographic record
Abstract
Abstract Objectives The objectives of this study were to investigate recent trends in non‐invasive prenatal testing (NIPT) utilisation, including factors associated with geographical variation, and to determine whether maternal or regional characteristics are associated with uptake the of NIPT. Methods This retrospective cohort study included pregnant individuals in Ontario with an expected date of delivery from August 1st, 2016 to March 31st, 2020. Modified Poisson regression was used to estimate rate ratios for NIPT use adjusted for maternal and healthcare covariates. Results We found substantial variation in NIPT uptake between regions within the province. The highest uptake was found in urban areas, highest quintile of neighbourhood income and education, for those who were ≥40 years of age and had a history of previous aneuploidy, for those with a prenatal care visit in the first trimester, multiple pregnancy, multigravidity and body mass index within the normal range (18.5–24.9 kg/m 2 ). Conclusion Our study demonstrated significant regional and maternal differences in NIPT uptake across Ontario. Given the large sample size and diverse population, our study may have implications for other jurisdictions with large, socio‐demographically and geographically diverse populations.
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How this classification was reachedexpand
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".