069 Assessing the impact of non-invasive prenatal screening (NIPS) on health-related quality of life: a comparative study using patient- reported outcome measures (PROMs) in prenatal screening among pregnant women
Bibliographic record
Abstract
Introduction The integration of Non-Invasive Prenatal Screening (NIPS) as a universal first-tier screening could provide an earlier screening and if needed, diagnostic result compared to standard screening. While the clinical performance of NIPS is well-documented, little research has documented its impact on pregnant women health-related quality of life (HRQoL) thus hampering informed decision- making about prenatal options. We compared the impact of NIPS on HRQoL to standard care. Methods In a prospective multicentric randomized trial, pregnant women (≥19 years) undergoing foetal chromosomal anomaly screening (T13, T18, T21) were randomized 2:1 to first-tier NIPS or standard care. Randomization considered age and recruitment site. HRQOL was assessed using PROM Physical Health and Mental Health summary scores at 10, 16, and 22 weeks of gestation. Descriptive and multivariate analyses, mainly using a linear mixed model, were performed. Results A total of 7815 pregnant women have been recruited with mean age of 32 years (Standard Deviation=4) and 64.7% of European origin in both groups. At 10, 16, and 22 weeks of gestation, physical Health and Mental Health summary scores were 47.2 to 53.0 (± 6 to 8) in both arms. The mean differences between first-tier NIPS group and standard care group were -0.77 (95% CI 2.06–0.52; P=0.12), for the physical health and 0.8034 (95% CI 0.42–2.02, P=0.10) for the mental health. Discussion Trajectories of physical and mental health scores were similar between groups. No significant differences were detected between the two groups. Conclusions The findings provide evidence that first-tier NIPS as an option for prenatal screening doesn’t worsen the HRQOL over 22 weeks of gestation compared to standard care. This evidence will be informing future decision aids about prenatal 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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".