Risks for Preeclampsia and Small for Gestational Age: Predictive Values of Placental Growth Factor, Soluble fms-like Tyrosine Kinase-1, and Inhibin A in Singleton and Multiple-Gestation Pregnancies
Bibliographic record
Abstract
OBJECTIVE: To determine the accuracy of placental growth factor (PlGF), soluble fms-like tyrosine kinase-1 (sFlt-1), and inhibin A in singleton and multiple-gestation pregnancies for predicting preeclampsia (PE) and small for gestational age (SGA). STUDY DESIGN: A prospective cohort nested in a randomized controlled trial of antioxidant supplementation for the prevention of PE. Plasma biomarkers were evaluated at 12 to 18 (visit 1) and 24 to 26 (visit 2) weeks' gestation and expressed as adjusted multiples of the median. RESULTS: Multiple-gestation pregnancy (74/772) had a significant impact on all biomarkers' levels. PlGF was the best predictor of PE and SGA. At a 10% false-positive rate, PlGF at visit 1 had 21% sensitivity for predicting PE in singleton versus 60% in multiple-gestation pregnancies. PlGF at visit 1 had a 31% sensitivity in singleton and 27% in multiple-gestation pregnancies for SGA prediction. CONCLUSION: PlGF level was a good predictor of subsequent PE as early as 12 to 18 weeks in multiple-gestation pregnancies but was not clinically useful enough to be used as a single marker.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".