Placental growth factor as a marker of fetal growth restriction caused by placental dysfunction
Bibliographic record
Abstract
INTRODUCTION: Discriminating between placentally-mediated fetal growth restriction and constitutionally-small fetuses is a challenge in obstetric practice. Placental growth factor (PlGF), measurable in the maternal circulation, may have this discriminatory capacity. METHODS: Plasma PlGF was measured in women presenting with suspected fetal growth restriction (FGR; ultrasound fetal abdominal circumference <10th percentile for gestational age) at sites in Canada, New Zealand and the United Kingdom. When available, placenta tissue underwent histopathological examination for lesions indicating placental dysfunction, blinded to PlGF and clinical outcome. Lesions were evaluated according to pre-specified severity criteria and an overall severity grade was assigned (0-3, absent to severe). Low PlGF (concentration <5th percentile for gestational age) to identify placental FGR (severity grade≥2) was assessed and compared with routine parameters for fetal assessment. For all cases, the relationship between PlGF and the sampling-to-delivery interval was determined. RESULTS: Low PlGF identified placental FGR with an area under the receiver-operator characteristic curve of 0.96 [95% CI 0.93-0.98], 98.2% [95% CI 90.5-99.9] sensitivity and 75.1% [95% CI 67.6-81.7] specificity. Negative and positive predictive values were 99.2% [95% CI 95.4-99.9] and 58.5% [95% CI 47.9-68.6], respectively. Low PlGF outperformed gestational age, abdominal circumference and umbilical artery resistance index in predicting placental FGR. Very low PlGF (<12 pg/mL) was associated with shorter sampling-to-delivery intervals than normal PlGF (13 vs. 29.5 days, P < 0.0001). DISCUSSION: Low PlGF identifies small fetuses with significant underlying placental pathology and is a promising tool for antenatal discrimination of FGR from fetuses who are constitutionally-small.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".