Preeclampsia Screening Taking Into Account Ethnicity and Socioeconomic Status—A Comparison of the Competing Risks Model and Risk Factor Scoring
Bibliographic record
Abstract
OBJECTIVES: This study aimed to compare preeclampsia (PE) risk screening by risk factors and the multivariable competing risks model. METHODS: This prospective cohort study enrolled singleton pregnancies, without major anomalies, and delivering at ≥24 weeks. PE risk was compared between the Fetal Medicine Foundation (FMF) model and clinical risk factors by National Institute for Health and Care Excellence (NICE) guidance, U.K. and "NICE-modified" by adding Black ethnicity and social deprivation (index of multiple deprivation deciles 1-4) as moderate risk factors. To compare screening strategies, we matched the FMF screen-positive rate (SPR) to NICE. RESULTS: At 11-13 weeks, preterm PE risk was assessed in 44 813 pregnancies; 368 (0.8%) developed preterm PE. At SPR = 7.4%, FMF (vs. NICE) almost tripled preterm PE detection rate (DR) but by more (by 19.8%) among Black women. The FMF model at SPR = 7.4% had DR = 67.7% for preterm PE, similar to NICE-modified screening (67.4%, which had SPR = 40.1%). At 35-36 weeks, subsequent PE risk was assessed in 29 035 pregnancies; 654 (2.3%) developed PE. At SPR = 10.9%, FMF (vs. NICE) more than doubled subsequent PE DR, regardless of index of multiple deprivation or Black ethnicity. FMF at SPR = 10.9% had DR for subsequent PE at least as high (70.5%) as NICE-modified screening (61.5%), which had SPR = 37.4%. CONCLUSIONS: The FMF model detects PE risk similar to risk factor-based screening, with addition of Black ethnicity and social deprivation as moderate risk factors but at substantially lower SPR at 11-13 weeks when aspirin is offered to prevent preterm PE and at 35-36 weeks when timed birth at term may prevent term PE.
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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 | 0.040 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".