Canadian women’s attitudes toward noninvasive prenatal testing of fetal DNA in maternal plasma*
Bibliographic record
Abstract
OBJECTIVE: To determine the perceptions and attitudes of Canadian women to Noninvasive Prenatal Testing of fetal DNA. STUDY DESIGN: A designed questionnaire was administered to women attending the outpatient antenatal clinic at a tertiary urban hospital. Attitudes to current and new prenatal screening modalities were assessed using a five-point Likert scale. Bowker's test of symmetry was used to compare individual responses regarding the two screening modalities. Changes in women's responses pre- and post-delivery were also compared. RESULTS: One hundred and twenty-nine women were enrolled in this study. 88% of women state that they would perform prenatal screening via fetal DNA in the maternal plasma if available. When compared to conventional screening, significantly less women believe that the NIPT should be available upon request for non-medical traits (36.4% versus 60.4%, p < 0.001). When compared to their answer before delivery, more women agreed that screening with fetal DNA in maternal plasma could be used in a negative way to select for desired non-medical traits such as gender. CONCLUSIONS: The use of fetal DNA in the maternal plasma is widely accepted in our Canadian population as a future method of noninvasive prenatal screening despite recognition of certain ethical concerns. This information can be used when implementing new genetic screening programs.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".