Association of Low Levels of Placental Growth Factor and Fetal Growth Restriction in a Setting With a High Prevalence of Diabetes [ID 2683431]
Bibliographic record
Abstract
INTRODUCTION: Placental growth factor (PlGF) is a glycosylated protein from the vascular endothelial growth factor (VEGF) family that is involved in placental angiogenesis. In cases of placental dysfunction, decreased maternal serum PlGF levels predict the development of pregnancy complications such as preeclampsia, fetal growth restriction (FGR), and stillbirth. METHODS: Datasets from 114 high-risk patients who had clinical indication for PlGF testing from 2021 to 2023 were collected retrospectively. The anonymized dataset comprises PlGF test results categorized by gestational age-specific ranges and FGR categorized by percentile. Chi-squared analysis of 114 patients was used to assess the association between PlGF levels and FGR (less than 10th percentile). This study was approved by REb-USask #Bio3702. RESULTS: Of the 114 patients, 21 (18.4%) with low/very low PlGF and 8 (7.1%) with borderline/normal PlGF had fetuses with FGR less than 10th percentile. There was a significant difference between groups with a low/very low PlGF and normal PlGF levels (P<.001). The negative predictive value (NPV) for development of FGR less than 10% was 97.54%. CONCLUSION: Data collection is ongoing; however, in our patient population with a high prevalence of diabetes, preliminary data from 114 patients suggest that gestational age-stratified maternal serum PlGF levels have high NPV value for development of FGR less than 10th percentile. The implementation of this simple laboratory test in rural and remote communities would allow for earlier identification of pregnancies that require more intensive surveillance at a high-risk center of care.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".