Implementation of Placental Growth Factor in a Tertiary Western Canadian Centre: Association with Ultrasound Findings and Perinatal Outcomes
Bibliographic record
Abstract
Objective To evaluate the association between placental growth factor (PlGF) levels, ultrasound findings, and perinatal outcomes in a high-risk pregnant population at a tertiary referral centre in Western Canada, and to assess the predictive performance of the PlGF test. Methods We conducted a retrospective cohort study of 389 high-risk pregnant individuals who underwent PlGF testing between 12+0 and 36+0 weeks' gestation. Participants were stratified by PlGF levels: normal (≥ 10th centile), inconclusive (5th–9th centile), and low (≤ 5th centile). Clinical, biochemical, ultrasound, and perinatal outcomes were compared across groups. Odds ratios, sensitivity, specificity, and predictive values were calculated. Results Low PlGF levels were observed in 33.9% pregnancies, with testing performed at a median gestational age of 27.7 weeks. Low PlGF levels were significantly associated with higher maternal BMI, elevated blood pressure, and increased creatinine, uric acid, and proteinuria levels. Ultrasound findings in the low PlGF group revealed higher rates of fetal growth restriction, abnormal Doppler studies, and abnormal placental morphology. These pregnancies had increased incidence of preterm birth <34 weeks (52/132 39.3%), preeclampsia (69/132 52.3%), NICU admissions (54/132 40.9%), and small-for-gestational-age neonates (15/132 11.4%). Most negative predictive values exceeded 90%. Conclusion Low maternal PlGF levels are strongly associated with ultrasound and biochemical indicators of placental dysfunction and adverse perinatal outcomes. PlGF testing may serve as an effective risk stratification tool in high-risk pregnancies, particularly in rural and underserved populations.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".