Placental Growth Factor for the Prediction of Adverse Outcomes in Patients with Suspected Preeclampsia or Intrauterine Growth Restriction
Bibliographic record
Abstract
BACKGROUND: The circulating concentration of PlGF is reported to be lower in patients experiencing preeclampsia and patients delivering a small for gestational age (SGA) neonate. To evaluate the predictive value of circulating PlGF for preeclampsia and adverse outcome in patients with suspected preeclampsia or intrauterine growth restriction (IUGR). METHODOLOGY/PRINCIPAL FINDINGS: A double blind prospective study. We enrolled 96 women for suspected preeclampsia or IUGR, and measured plasma levels of PlGF (Triage®) at enrolment. We defined adverse outcome as severe preeclampsia, SGA neonate (<10(th) centile) or elective delivery for maternal or fetal complication. Severe adverse outcome was studied among patients included <34 weeks gestation (WG) and defined as eclampsia, HELLP syndrome, very SGA (<3(rd) centile) or elective delivery <34 WG. The mean logtransformed PlGF level was lower for women who experienced preeclampsia (2.9 vs 3.7, p = 0.02), and was markedly lower for patients who experienced adverse outcome (2.9 vs 4.3, p<0.001). The odds of presenting an adverse outcome were higher for the lowest tertile of PlGF compared to the higher (OR = 13 , 95% CI [3-50]). For severe adverse outcome, odds were respectively for the lowest and intermediate tertile as compared with the higher tertile : OR = 216, 95% CI [18-2571]; and OR = 17, 95% CI [3-94]. When included <34 WG, patients with a PlGF level <12 pg/ml experienced a severe adverse outcome in 96% of cases (24/25), and only 1 of 20 patients with a PlGF level >5(th) centile experienced a severe adverse outcome within 15 days (5%). CONCLUSIONS/SIGNIFICANCE: Among women with suspected preeclampsia or IUGR, PlGF helps identify women who will experience an adverse outcome and those who will not within a time period of 15 days.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".