Accuracy of circulating placental growth factor, vascular endothelial growth factor, soluble fms‐like tyrosine kinase 1 and soluble endoglin in the prediction of pre‐eclampsia: a systematic review and meta‐analysis
Bibliographic record
Abstract
Please cite this paper as: Kleinrouweler C, Wiegerinck M, Ris‐Stalpers C, Bossuyt P, van der Post J, von Dadelszen P, Mol B, Pajkrt E, for the EBM CONNECT Collaboration. Accuracy of circulating placental growth factor, vascular endothelial growth factor, soluble fms‐like tyrosine kinase 1 and soluble endoglin in the prediction of pre‐eclampsia: a systematic review and meta‐analysis. BJOG 2012;119:778–787. Background Biomarkers have been proposed for identification of women at increased risk of developing pre‐eclampsia. Objectives To investigate the capacity of circulating placental growth factor (PlGF), vascular endothelial growth factor (VEGF), soluble fms‐like tyrosine kinase‐1 (sFLT1) and soluble endoglin (sENG) to predict pre‐eclampsia. Search strategy Medline and Embase through October 2010 and reference lists of reviews, without constraints. Selection criteria We included original publications on testing of PlGF, VEGF, sFLT1 and sENG in serum or plasma of pregnant women at <30 weeks of gestation and before clinical onset of pre‐eclampsia. Data collection and analysis Two reviewers independently identified eligible studies, extracted descriptive and test accuracy data and assessed methodological quality. Summary estimates of discriminatory performance were obtained. Main results We included 34 studies. Concentrations of PlGF (27 studies) and VEGF (three studies) were lower in women who developed pre‐eclampsia: standardised mean differences (SMD) −0.56 (95% CI −0.77 to −0.35) and −1.25 (95% CI −2.73 to 0.23). Concentrations of sFLT1 (19 studies) and sENG (ten studies) were higher: SMD 0.48 (95% CI 0.21–0.75) and SMD 0.54 (95% CI 0.24–0.84). The summary diagnostic odds ratios were: PlGF 9.0 (95% CI 5.6–14.5), sFLT1 6.6 (95% CI 3.1–13.7), sENG 4.2 (95% CI 2.4–7.2), which correspond to sensitivities of 32%, 26% and 18%, respectively, for a 5% false‐positive rate. Author’s conclusions PlGF, sFLT1 and sENG showed modest but significantly different concentrations before 30 weeks of gestation in women who developed pre‐eclampsia. Test accuracies of all four markers, however, are too poor for accurate prediction of pre‐eclampsia in clinical practice.
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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 | 0.020 | 0.057 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.036 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".