First-Trimester Placental Growth Factor for the Prediction of Preeclampsia in Nulliparous Women: The Great Obstetrical Syndromes Cohort Study
Bibliographic record
Abstract
BACKGROUND: First-trimester maternal serum markers have been associated with preeclampsia (PE). We aimed to evaluate the performance of first-trimester placental growth factor (PlGF) for the prediction of PE in nulliparous women. SUBJECTS AND METHODS: We conducted a prospective cohort study of nulliparous women with singleton pregnancy at 11-13 weeks. Maternal serum PlGF concentration was measured using B·R·A·H·M·S PlGFplus KRYPTOR automated assays and reported in multiple of the median adjusted for gestational age. We used proportional hazard models, along with receiver operating characteristic curves and areas under the curve (AUC). RESULTS: Out of 4,652 participants, we observed 232 (4.9%) cases of PE including 202 (4.3%) term and 30 (0.6%) preterm PE. PlGF was associated with the risk of term (AUC = 0.61, 95% confidence interval [CI] 0.57-0.65) and preterm PE (AUC = 0.73, 95% CI 0.64-0.83). The models were improved with the addition of maternal characteristics (AUC for term PE 0.66, 95% CI 0.62-0.71; AUC for preterm PE 0.81, 95% CI 0.72-0.91; p < 0.01). At a false-positive rate of 10%, PlGF combined with maternal characteristics could have predicted 26% of term and 55% of preterm PE. The addition of pregnancy-associated plasma protein A did not significantly improve the prediction models. CONCLUSION: First-trimester PlGF combined with maternal characteristics is useful to predict preterm PE in nulliparous women.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".