8.3 Placental growth factor (PLGF) and ultrasound parameters for predicting the small for gestational age infant (SGA) in suspected small for gestational age: pelican FGR study
Bibliographic record
Abstract
The SGA infant is at increased risk of short and long-term health complications. Identifying the SGA fetus remains challenging. Placental insufficiency contributes to pathogenesis and it has been proposed that markers of placental function such as PlGF may improve prediction. PELICAN FGR, a large multicentre, prospective observational study measuring PlGF in women with reduced symphysis fundal height (SFH), assessed the ability of PlGF and ultrasound parameters to predict delivery of an SGA infant. 592 women with singleton pregnancies and reduced SFH between 24–37 weeks’ gestation across 11 sites in UK and Canada were analysed. Plasma PlGF was measured at enrolment and ultrasound data recorded. Plasma PlGF concentration <5th centile, estimated fetal weight <10th centile (EFW10), umbilical artery Doppler pulsatility index > 95th centile and oligohydramnios were compared as predictors for an SGA infant <3rd (SGA3) and <10th (SGA10) customised birthweight centiles. Test performance statistics were calculated for all parameters in isolation and combination. Sensitivity and negative predictive value (NPV) of EFW10 for SGA3 (n = 78) were 61% and 93% respectively; for SGA10 (n = 192) they were 49% and 77% respectively. PlGF had sensitivity of 37% and NPV of 90% for SGA3. In combination, PlGF and EFW10 had sensitivity of 69% and NPV of 93% for SGA3. In women presenting with reduced SFH, EFW10 and other ultrasound parameters have modest test performance for prediction of a subsequent SGA infant. PlGF performed no better than EFW10 in determining delivery of an SGA infant. Incorporating PlGF with ultrasound parameters provided modest improvements in test performance.
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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 | 0.001 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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".