The Utility of Maternal Blood S100B in Women with Suspected or Established Preeclampsia—A Systematic Review
Bibliographic record
Abstract
PURPOSE: Preeclampsia is a serious pregnancy complication without curative treatment. The central nervous system (CNS) is affected in severe cases of preeclampsia. Until now, no biomarker or other predictive method has been established for predicting severe CNS injury, including the development of eclampsia and/or long-term complications. In this systematic review, we aimed to investigate the association between maternal blood (serum or plasma) S100B levels and preeclampsia, focusing on its predictive value and correlation with the severity of the disease, with a particular focus on neurological symptoms. METHODS: A search of online databases, including Medline via PubMed, Scopus databases, and Web of Science, was performed based on the PRISMA guidelines for systematic reviews. RESULTS: Ten case-control studies that met the inclusion criteria were identified and further evaluated according to the Newcastle-Ottawa Scale (NOS). All of the studies revealed that S100B blood levels were higher in preeclampsia compared to uncomplicated pregnancies before onset, after its diagnosis, and one year postpartum. Its predictive value seems to be adequate long before the onset of preeclampsia, especially in the early third trimester. Furthermore, its levels seem to correlate with severe complications during pregnancy, such as eclampsia and HELLP syndrome, as well as neurological dysfunction postpartum. CONCLUSIONS: S100B is a promising biomarker for the prediction of acute and long-term CNS injury in preeclampsia. Still, additional studies should be conducted in order to establish a standard method of measurement and solidify its clinical use in preeclampsia management, providing individualized care in order to improve perinatal outcomes and provide personalized follow-up postpartum.
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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 | 0.004 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".