Utility of placental growth factor for preeclampsia prediction in pregnancies complicated by sickle cell disease
Bibliographic record
Abstract
ABSTRACT: Outside of pregnancy, placental growth factor (PlGF), is produced by erythroid cells in typically undetectable levels. In pregnancy, PlGF is strongly expressed by the trophoblast layer covering the placental villi. PlGF levels rise progressively due to placental growth, peak at 28 to 30 weeks gestation, and then slowly decline toward term. Low PlGF has emerged as a powerful diagnostic test for preterm preeclampsia. However, its interpretation in context of sickle cell disease (SCD) is potentially confounded by upregulation of cellular PlGF expression in nonpregnant individuals with SCD, and higher third trimester circulating PlGF levels documented in healthy Black compared with White individuals. Primary objectives were to determine the distribution of PlGF at midtrimester in pregnant individuals with SCD compared with unaffected Black controls and to explore the diagnostic accuracy of PlGF in the context of suspected preeclampsia in pregnancies of individuals with SCD. Secondary objective was to examine the relationship between low PlGF and placental disease in pregnancies of individuals with SCD. Pregnant individuals with SCD at Mount Sinai Hospital in Canada (January 2017 to September 2021) with at least 1 PlGF measurement 20+0 to 35+6 weeks gestation, and pregnant Black controls without SCD with suspected preeclampsia or growth restriction, were included in this retrospective study. Maternal and neonatal outcomes were extracted from medical records. For early-onset, but not late-onset, preeclampsia, a PlGF cutoff of <100 pg/mL demonstrated 100% sensitivity and specificity at 20 to 24 weeks gestation. This study is, to our knowledge, the first to demonstrate the utility of PlGF in predicting early-onset preeclampsia in pregnancies of individuals with SCD, allowing clinicians to anticipate and mitigate adverse pregnancy outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| 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 | 0.000 |
| 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".