A Comparative Study Using Patient-Reported Experience Measures (PREMs) in Prenatal Screening Among Pregnant Women in Canada
Bibliographic record
Abstract
Context The non-invasive prenatal screening (NIPS) has higher sensitivity and specificity in detecting trisomy 21, 18 or 13 than standard care screening, thereby reducing the need for more invasive confirmatory tests that can result in pregnancy loss. The turnaround time for results is also shorter when NIPS is used as a first-tier test compared to standard care. Patient-reported experience measures (PREMs) allow for the evaluation of care quality from the patient9s perspective. Objective Compare the experience of pregnant women undergoing first-tier NIPS with those undergoing standard care. Study Design and Analysis This study is a secondary analysis using data from a prospective, open-label, multicenter randomized trial. Setting or Dataset Data was collected in two Canadian provinces (QC and BC) from 2020 to 2022. At 10 weeks of pregnancy, pregnant women provided their socio-demographic data, between 10 and 13 weeks, they received prenatal screening and at 22 weeks of pregnancy, they were invited to complete the PREMs questionnaire, which assesses patient experiences. Population Studied Pregnant women aged 19 and older. Intervention/Instrument Pregnant women were randomized 2:1 to first-tier NIPS or standard care for prenatal screening of chromosomal anomalies T21, T18, or T13. The administered questionnaire consists of 17 questions, 10 items being on a 5-point Likert scale. Following the validation of the questionnaire through exploratory and confirmatory factor analyses, 7 items were retained and grouped into two distinct factors (i.e. human experience of the process and perception of the healthcare professional9s technical competence). Outcome Measures Linear regression models compared the patient experiences between the two distinct treatment groups using the 2 factors as outcomes. Results Of the 7815 pregnant women enrolled from 5 clinical sites, 6050 were included for comparison, with 4213 participants in the intervention group and 1837 in the control group. The mean age was 32.1 (±4.0). The overall response rate was 80% and the overall completion rate was 96%. First-tier NIPS has converged positively with the factor score assessing the perception of the healthcare professional’s technical competence (β=1.28,P=0.02). Conclusions First-tier NIPS testing enhanced patient experiences by mitigating the uncertainty associated with prenatal screening processes and fostering the perception of healthcare professionals’ technical competence.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".