sFlt-1, Coagulation Function, and Platelets as Predictors of Preeclampsia
Bibliographic record
Abstract
Objective To investigate the predictive value of soluble FMS-like tyrosine kinase-1 (sFlt-1), coagulation function, and platelet (PLT) parameters for preeclampsia (PE). Methods A prospective study was conducted on women registered and delivered at Shanghai Fifth People's Hospital from October 2020 to December 2021. All eligible pregnant women were recruited at the time of initial registration in the first trimester. We then obtained serum samples uniformly at 24 0 –28 0 weeks and stored these samples in a freezer at −80°C and labelled them to create a biobank. Later, when PE was diagnosed, we followed the markers to find their blood samples and complete the tests. Participants were divided into healthy pregnant (HP) and PE groups. Participants were divided into HP and PE groups. Approximately 5 mL of venous blood was collected from each participant at 24 0 –28 0 weeks gestation. Serum sFlt-1 was measured by enzyme-linked immunosorbent assay. Additionally, D-dimer, activated partial thromboplastin time (APTT), thrombin time (TT), prothrombin time (PT), antithrombin III (ATIII), fibrinogen, PLT, PLT distribution width (PDW), and mean PLT volume (MPV) were recorded. SPSS 27.0 software was used to analyze the correlation of these parameters with PE. Receiver operating characteristic curve analysis determined the optimal cutoff value for each parameter. Results Serum sFlt-1, APTT, TT, ATIII, PLT, MPV, and PDW levels were significantly different between the PE and HP groups ( P < 0.05). Among single-factor indicators for predicting PE, sFlt-1 exhibited the highest value. With an optimal cutoff value of 4.409 ng/mL, sFlt-1 demonstrated a sensitivity and specificity of 85.4% and 87.5%, respectively. The combination of sFlt-1, APTT, TT, PDW, and MPV yielded the highest predictive value, with an area under the receiver operating characteristic curve of 0.946, sensitivity of 86.8%, and specificity of 87.5%. Conclusions This study demonstrates that a combination of sFlt-1, APTT, TT, PDW, and MPV is a valuable tool for predicting PE.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".