What knowledge is required for an informed choice related to non‐invasive prenatal screening?
Bibliographic record
Abstract
Non-invasive prenatal screening (NIPS) using cell-free DNA is a screening test for fetal aneuploidy offered by a variety of prenatal healthcare providers. Guidelines for genetic screening consistently recommend that providers facilitate informed choices, which have been associated with better psychological and clinical outcomes than uninformed choices. The multidimensional measure of informed choice (MMIC) is a widely used and theory-based measure that combines knowledge, values, and behavior to classify decisions as either informed or uniformed. We implemented a previously validated version of the MMIC for women offered NIPS to describe the choices made by women receiving prenatal care at the Vanderbilt University Medical Center. The survey included the Ottawa Decisional Conflict scale, an outcome measure used for validation of choice categorization. We found that most women (87%) made an informed choice about NIPS. Of the women categorized as uninformed, 67% had insufficient knowledge, and 33% had an attitude discordant with their decision. The vast majority of respondents (92.5%) underwent NIPS and had a positive attitude toward screening (94.3%). Ethnicity (p = 0.04) and education (p = 0.01) were found to be significantly associated with informed choice. Decisional conflict was extremely low among all participants, with only 5.6% of all participants demonstrating any form of decisional conflict, and all being categorized as having made an informed choice. This study suggests that pre-test counseling by a genetic counselor results in high rates of informed choice and low-decisional conflict amongst women offered NIPS by genetic counselors, though more research is required to determine if rates of informed choice remain high when NIPS is offered by other prenatal providers.
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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 | 0.008 | 0.075 |
| 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 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".