Preparation for prenatal decision-making: a baseline of knowledge and reflection in women participating in prenatal screening
Bibliographic record
Abstract
OBJECTIVE: This prospective study gathered baseline information about knowledge and intentions regarding prenatal testing from women attending their nuchal translucency (NT) ultrasound and first sequential blood screen. METHOD: Surveys including questions about pregnancy history and hypotheticals about fetal diagnoses were distributed to all willing participants at an urban hospital and a suburban medical building during the waiting time for the NT (N = 659). RESULTS: The majority "never thought anything could be wrong" with their fetus and had not talked with the father or the health providers about that possibility. Presented with varied fetal diagnoses, the larger group nearly always "had the baby", except in the case of a fatal condition where 28% said they would have the baby in contrast to 26% who would end the pregnancy (remainder undecided). Hypotheticals about varied fetal conditions were generally "undecided" by a quarter to nearly half of the respondents. CONCLUSION: Women's baseline knowledge and reflection about the nature of prenatal screening and diagnosis are minimal in contrast to the large impact positive results could have on their lives. Providers need to weigh the benefit of priming decision-making by exposing women to the possibility of fetal diagnosis, against the cost of raised anxiety.
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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 | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".