Clinical Validation of the sFlt-1:PlGF Ratio as a Biomarker for Preeclampsia Diagnosis in a High-Risk Obstetrics Unit
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Bibliographic record
Abstract
BACKGROUND: Preeclampsia is a multisystem disorder defined by new onset of hypertension with proteinuria after 20 weeks gestation. In part due to dysregulation of pro-angiogenic factors (e.g., placental growth factor [PlGF]) and anti-angiogenic factors (e.g., soluble fms-like tyrosine kinase 1 [sFlt-1]), preeclampsia results in decreased placental perfusion. An increased sFlt-1:PlGF ratio is associated with increased risk of preeclampsia. In this study, we evaluated sFlt-1:PlGF cutoffs and evaluated the clinical performance of sFlt-1:PlGF for predicting preeclampsia. METHODS: sFlt-1:PlGF results from 130 pregnant females with clinical suspicion of preeclampsia were used to evaluate the diagnostic accuracy of different sFlt-1:PlGF cutoffs and to compare the clinical performance of sFlt-1:PlGF to traditional preeclampsia markers (proteinuria and hypertension). Serum sFlt-1 and PlGF were measured using Elecsys immunoassays (Roche Diagnostics) and preeclampsia diagnosis was verified by expert chart review. RESULTS: A sFlt-1:PlGF cutoff of >38 yielded the greatest diagnostic accuracy of 90.8% (95% CI, 85.8%-95.7%). Using a cutoff of >38, sFlt-1:PlGF exhibited a greater diagnostic accuracy than traditionally used parameters such as new or worsening proteinuria or hypertension (71.9% and 68.6%, respectively). sFlt-1:PlGF >38 exhibited a negative predictive value (NPV) of 96.4% for rule-out of preeclampsia within 7 days, and a positive predictive value (PPV) of 84.8% for predicting preeclampsia within 28 days. CONCLUSIONS: Our study shows the superior clinical performance of sFlt-1:PlGF over hypertension and proteinuria alone to predict preeclampsia at a high-risk obstetrical unit.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| 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 it