First-trimester preeclampsia screening and prevention: impact on patient satisfaction and anxiety
Bibliographic record
Abstract
BACKGROUND: Preeclampsia affects between 2% and 5% of pregnant people in North America. First-trimester preeclampsia screening based on the Fetal Medicine Foundation risk calculation algorithm combined with treatment of high-risk patients with aspirin effectively reduces the incidence of preterm preeclampsia more than the currently used risk factor-based screening. However, the impact of such screening on patient satisfaction and maternal anxiety is unknown. OBJECTIVE: This study aimed to assess the impact of first-trimester prediction and prevention of preterm preeclampsia on patient satisfaction and anxiety. STUDY DESIGN: were contacted 6 weeks postpartum to complete an online patient satisfaction survey, designed to assess their satisfaction with the screening program and their levels of trait anxiety (using an abbreviated version of the State-Trait Anxiety Inventory [STAIT-5]). In addition to assessing overall patient satisfaction, the level of patient satisfaction was stratified and compared according to levels of patient risk for preterm preeclampsia. RESULTS: Between June 2021 and December 2021, surveys were emailed to 765 participants. The response rate was 47.80% (358/765). Overall, 93% of participants reported high levels of satisfaction with preterm preeclampsia screening (70%-100%), and 98% stated that they would recommend the screening to all pregnant patients. With respect to levels of satisfaction with the program's support in reducing feelings of worry and anxiety, 87.9% of the total sample reported high satisfaction (70%-100%). The level of clinically significant symptoms of anxiety did not differ significantly between low- and high-risk groups (8% vs 10.8%, respectively). CONCLUSION: Overall, first-trimester preeclampsia screening was associated with high patient satisfaction and did not lead to differences in patient anxiety between those with high- and low-risk screen results.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".