PlGF (Placental Growth Factor) Testing in Clinical Practice: Evidence From a Canadian Tertiary Maternity Referral Center
Bibliographic record
Abstract
There is little evidence evaluating angiogenic growth factor testing in real-world obstetric settings. This investigation evaluated maternal and perinatal pregnancy outcomes associated with maternal PlGF (placental growth factor) levels in real-world clinical care of high-risk pregnancies. From March 2017 to December 2019, 979 pregnant women with suspected risk of placental dysfunction, hypertensive disorders of pregnancy, or fetal growth restriction completed PlGF testing between 20+0 and 35+6 weeks of gestation. Maternal, fetal, and delivery characteristics were extracted through the electronic medical record system. The primary outcome of preterm birth was assessed using Royston-Parmar survival models and summarized with Kaplan-Meier methods. Of the 979 pregnant women, 289 had low PlGF levels (29.5%), and 690 had normal PlGF levels (70.5%). The survival probability of ongoing pregnancy free from preterm birth within 2- and 4-weeks following PlGF testing was significantly reduced in women with low PlGF levels, relative to women with normal PlGF levels (0.57 versus 0.99, standardized survival difference, −0.43 [95% CI, −0.76 to −0.09], and 0.37 versus 0.99, standardized survival difference, −0.62 [95% CI −0.87 to −0.38], respectively). Women with low PlGF levels were more likely to develop early-onset preeclampsia (adjusted odds ratio, 58.2 [95% CI, 32.1–105.4]) and have a stillbirth (adjusted odds ratio, 15.9 [95% CI, 7.6–33.3]). PlGF status distinguished placental from fetal causes of stillbirth. Low PlGF levels in high-risk pregnant women are strongly associated with increased rates of imminent preterm birth, as well as related adverse outcomes, including early-onset preeclampsia and stillbirth.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.000 | 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 teacher head, 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".