Screening for preeclampsia in low‐risk twin pregnancies at early gestation
Bibliographic record
Abstract
INTRODUCTION: Preeclampsia affects about 10% of twin pregnancies and significantly increases the risk of adverse pregnancy outcomes. However, screening models for preeclampsia in twin pregnancies remain elusive. The present study aimed to evaluate the performance of a multi-marker first trimester preeclampsia screening model in low-risk twin pregnancies. MATERIAL AND METHODS: Between 2014 and 2017, we prospectively assessed first trimester biomarkers for preeclampsia in a 'low-risk' twin pregnancy cohort at a single center. Multiple logistic regression was used to determine significant predictors for early preeclampsia (occurring prior to 34 weeks) and late preeclampsia (occurring after 34 weeks). The performance of the screening models fitted using the significant predictors was calculated using receiver operating characteristics curves, and internal validation was performed using bootstrapping. RESULTS: A total of 769 twin pregnancies were included in the study. Early preeclampsia and late preeclampsia developed in 27 (3.5%) and 59 (7.7%) cases, respectively. Logistic regression analyses showed that maternal age, body mass index, mean artery pressure and placental growth factor were significant predictors for early preeclampsia. Maternal age, body mass index, mean artery pressure and pregnancy-associated plasma protein A were significant for late preeclampsia. Uterine artery pulsatility index was not predictive of either early or late preeclampsia. For the fitted screening model of early and late preeclampsia, the areas under receiver operating characteristics curves were 0.82 (95% confidence interval [CI] 0.76-0.88) and 0.66 (95% CI 0.59-0.73), which were expected to decrease to 0.77 and 0.60, respectively, based on bootstrapping; the positive predictive values were 10.2% and 12.5%; and the estimated detection rates were 40.7% and 22.0%, respectively, at a false-positive rate of 10%. CONCLUSIONS: A multi-marker screening model for preeclampsia in low-risk twin pregnancies, using a modified version of Fetal Medicine Foundation predictors in singletons, does not perform well. Uterine artery pulsatility index is of little value in screening for preeclampsia in low-risk twin pregnancies.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".