First-Trimester PlGF and PAPP-A and the Risk of Placenta-Mediated Complications: PREDICTION Prospective Study
Bibliographic record
Abstract
OBJECTIVES: This study aimed to estimate the association between low first-trimester maternal serum PlGF (placental growth factor) and PAPP-A (pregnancy-associated plasma protein A) and the risk of placenta-mediated complications. METHODS: We performed a secondary analysis of the PREDICTION study, including nulliparous participants recruited at 11 to 14 weeks of pregnancy. First-trimester PlGF and PAPP-A levels were reported in multiples of the median (MoM) adjusted for maternal characteristics and gestational age. Participants were stratified into 4 groups based on absence/presence of low (<0.4 MoM) PlGF and PAPP-A values. A composite of adverse pregnancy outcomes (including preeclampsia, fetal growth restriction, fetal death, and placental abruption) was calculated for deliveries occurring before 34 weeks, before 37 weeks, and at or after 37 weeks. RESULTS: Out of the 7262 participants, 86 (1.2%) experienced the composite outcome before 37 weeks of gestation, including 35 (0.4%) before 34 weeks. The combination of low PAPP-A and low PlGF levels was associated with the greatest risk of adverse outcomes before 37 weeks (21%) and before 34 weeks (12%) compared with low PlGF alone (7% and 3%), low PAPP-A alone (2% and 1%), or neither marker (1% and 0.4%, respectively; P < 0.001). For preterm preeclampsia specifically, the combination of low PAPP-A and low PlGF was also associated with a greater risk (12%) compared with low PlGF alone (6%), low PAPP-A alone (0.5%), or neither marker (0.7%; P < 0.001). CONCLUSIONS: The combination of low PAPP-A and low PlGF levels is associated with a very high risk for adverse outcomes before 34 and 37 weeks. An isolated low PAPP-A should not be considered a risk factor for adverse pregnancy outcomes.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".