OC08.01: Effective screening for pre‐eclampsia by maternal factors and biomarkers at 11–13 weeks' gestation
Bibliographic record
Abstract
The purpose of this study was to develop a competing risk model for pre-eclampsia (PE) based on maternal factors and biomarkers. The data for this study were derived from prospective screening for adverse outcomes in women who attended for their routine first scan at 11–13 weeks gestation in 2 maternity hospitals in England. We screened near 36,948 singleton pregnancies that included 1,058 pregnancies (2.9%) that developed PE. Bayes theorem was used to combine the a priori risk from maternal factors with various combinations of biophysical and biochemical markers multiple of the median values. Five-fold cross validation was used to assess the performance of screening for PE that delivered at <37 weeks gestation (preterm-PE) and ≥37 weeks gestation (term-PE) by models that combined maternal factors with individual biomarkers and their combination with screening by maternal factors alone. In pregnancies that developed PE the values of uterine artery pulsatility index (PI) and mean arterial pressure (MAP) were increased, and the values of serum pregnancy-associated plasma protein-A (PAPP-A), and placental growth factor (PlGF) were decreased. For all biomarkers, the deviation from normal was greater for preterm than term-PE; therefore, the performance of screening was related inversely to the gestational age at which delivery became necessary for maternal and/or fetal indications. Combined screening by maternal factors, uterine artery PI, MAP, and PlGF predicted 75% (95% confidence interval [CI], 70-80%) of preterm-PE and 47% (95% CI, 44-51%) of term-PE, at a false-positive rate of 10%; inclusion of PAPP-A did not improve the performance of screening. Such detection rates are superior to the respective values of 49% (95% CI, 43-55%) and 38% (34-41%) that were achieved by screening with maternal factors alone. Combination of maternal factors and biomarkers provides effective first-trimester screening for preterm-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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".