Association of Low Levels of Placental Growth Factor With Abnormal Fetal Doppler Assessment [ID 2683423]
Bibliographic record
Abstract
INTRODUCTION: Placental growth factor (PlGF) is involved in placental angiogenesis and maturation. Low maternal serum PlGF levels predict placental dysfunction and associated conditions, such as preeclampsia, and fetal growth restriction. These pregnancy complications can lead to abnormal fetal Doppler assessment, indicating progressive fetal deterioration prompting preterm delivery. METHODS: We collected retrospective data from laboratory results of PlGF levels and fetal Dopplers (umbilical artery [UA], middle cerebral artery [MCA], and ductus venosus [DV]) from patients who had clinical indication for PlGF testing. Over 100 patients were identified to have results of all assessments between 2021 and 2023. Levels of PlGF were stratified in five categories according to gestational age and compared with normal and abnormal doppler results. For comparisons, the values were aggregated in normal and low PlGF. RESULTS: There were statistically significant associations between PlGF levels and Dopplers. Patients with low PlGF (less than 5th percentile for gestational age) were more likely to have abnormal dopplers than patients with normal PlGF (UA, P<.001; MCA, P<.001; DV, P<.001). A normal PlGF had a high negative predictive value (NPV) for normal dopplers (UA, 88%; MCA, 94.3%; DV, 98.6%). CONCLUSION: The results show that normal PlGF is highly associated with normal Dopplers. Additional data will be required to account for confounding factors; however, our preliminary data suggest that PlGF could be a useful tool for patients in rural and remote locations, as normal PlGF has a statistically and clinically significant negative predictive value for normal Doppler assessment.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".