Combining Biochemical and Ultrasonographic Markers in Predicting Preeclampsia: A Systematic Review
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Bibliographic record
Abstract
BACKGROUND: Early identification of pregnant women at risk for preeclampsia is a priority to implement preventive measures. Some biochemical and ultrasonographic parameters have shown promising predictive performance, but so far there is no clinically validated screening procedure. CONTENT: Using a series of keywords, we reviewed electronic databases (Medline, Embase, all records to May 2009) reporting the performance of biological and ultrasonographic markers to predict preeclampsia, both single markers and combinations of markers. We analyzed the data according to gestational age and risk levels of the studied populations. We evaluated the methodological quality of included publications using QUADAS (quality assessment of diagnostic accuracy studies). We identified 37 relevant studies that assessed 71 different combinations of biochemical and ultrasonographic markers. Most studies were performed during the second trimester on small-scale high-risk populations with few cases of preeclampsia. Combinations of markers generally led to an increase in sensitivity and/or specificity compared with single markers. In low-risk populations, combinations including placental protein 13 (PP13), pregnancy-associated plasma protein A (PAPP-A), a disintegrin and metalloprotease-12 (ADAM12), activin A, or inhibin A measured in first or early second trimester and uterine artery Doppler in second trimester appear promising (sensitivity 60%-80%, specificity >80%). In high-risk populations, the combination of PP13 and pulsatility index in first trimester showed 90% sensitivity and 90% specificity in a single study limited to severe preeclampsia. SUMMARY: Combinations of biochemical and ultrasonographic markers improved the performance of early prediction of preeclampsia. From a perspective of integrative medicine, large population-based studies evaluating algorithms combining multiple markers are needed, if screening approaches are to be eventually implemented.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.004 |
| 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