Placental Growth Factor (PlGF) associated with compromised Fetal Growth and Perinatal Outcomes in a High-Risk Pregnancy Population: A Retrospective Cohort
Bibliographic record
Abstract
Introduction: Fetal Growth Restriction (FGR) is associated with placental dysfunction. Placental Growth Factor (PlGF) can help in the prediction and timely diagnosis of FGR. This study aimed to evaluate the association between FGR, PlGF, and perinatal outcomes. Methods: Retrospective cohort of 292 patients. The primary exposure was maternal PlGF levels. Primary outcomes were fetal growth, abnormal sonographic placental morphology, preeclampsia, fetal demise (IUFD), preterm birth (PTB) < 34weeks, low birth weight, neonatal admission to NICU and placental pathology findings. Results: Normal-grown fetuses had longer pregnancies when compared to FGR pregnancies. Low PlGF levels were statistically significant and almost 5-fold higher among pregnancies with compromised fetal growth. There were 12.5-fold chance of IUFD in fetuses with compromised growth. Low birthweight was over ten times higher in growth-restricted fetuses. PTB < 34w and neonatal admission to NICU were also increased among patients with compromised fetal growth. Abnormal sonographic placental morphology was associated with fetal growth restriction. Preeclampsia was not associated with compromised fetal growth in this cohort. Abnormal placental pathology was increased 7-fold in growth-restricted fetuses. Conclusion: PlGF for the management of high-risk cases with compromised fetal growth is useful. The results confirm that compromised fetal growth is representative of placental dysfunction, associated with or without preeclampsia. In this context, PlGF testing has the potential to improve healthcare outcome in obstetrical care, especially in remote or low-resource settings.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".