Combining biochemical and ultrasonographic markers in predicting preeclampsia: a systematic review
Bibliographic record
Abstract
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 Ameasured 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. 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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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 | 0.016 | 0.051 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.005 |
| Bibliometrics | 0.035 | 0.031 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".