Placental growth factor as an indicator of fetal growth restriction in late‐onset small‐for‐gestational age pregnancies
Bibliographic record
Abstract
BACKGROUND: At-risk small-for-gestational age (SGA) pregnancies in New Zealand are identified using Doppler ultrasound; fetuses with Doppler abnormalities are considered growth restricted (FGR). Low maternal placental growth factor (PlGF) has also been associated with late-onset FGR. AIMS: To investigate whether low PlGF at diagnosis of late-onset SGA identifies the same fetuses classified FGR by detailed Doppler studies, and the association between low PlGF and adverse pregnancy outcomes. METHODS: Among an historical database of normotensive suspected SGA pregnancies (fetal abdominal circumference <10th percentile) ≥32 weeks gestation, the ability of low PlGF (<5th percentile) to identify FGR infants was investigated. 'Initial FGR' was an abnormal umbilical artery resistance index (RI) or estimated fetal weight <3rd customised centile. 'Secondary FGR' was abnormal internal carotid RI, cerebro-placental ratio and/or mean uterine artery RI. Development of hypertensive disease and adverse perinatal outcomes were compared by PlGF status. RESULTS: Of 136 SGA pregnancies, 56 (41.1%) had initial FGR. Of the remaining, 20 (25.0%) had secondary FGR, 17 (21.3%) low PlGF. The sensitivity of low PlGF identifying secondary FGR was 0.30 (95% CI 0.14-0.50), specificity 0.83 (0.70-0.92), positive predictive value 0.47 (0.23-0.72) and negative predictive value 0.70 (0.57-0.81). Overall, low PlGF occurred in 44/136 (32.4%) pregnancies and was associated with gestational hypertensive disease (63.6% vs 15.2%, P < 0.01), adverse perinatal outcome (34.1% vs 15.2%, P = 0.01) and very low birthweight (customised centile 2.2 vs 6.8, P < 0.01). CONCLUSIONS: At diagnosis of late-onset SGA, low PlGF was poor at identifying Doppler-defined FGR. Low PlGF identified pregnancies at risk of hypertensive disease, adverse perinatal outcome and very low birthweight.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 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".