Evaluation of the angiogenic factors sFlt‐1, PlGF, and the sFlt‐1/PlGF ratio in preeclampsia and associated features
Bibliographic record
Abstract
PROBLEM: Soluble Fms-like tyrosine kinase-1 (sFLT-1) and placental growth factor (PlGF) were previously reported to play a key role in the pathogenesis of preeclampsia (PE). We tested the link between altered PlGF and sFLT-1 levels, and their ratio (sFlt-1/PlGF) with PE and PE-associated featured in Tunisian PE cases and age- and BMI-matched normotensive women. METHOD OF STUDY: Peripheral blood specimens from 88 women with PE, and 60 control women were tested for PlGF and sFLT by commercially available ELISA. RESULTS: Significant increases in sFlt-1 levels and in the sFlt-1/PlGF ratio, more than changes in PlGF levels were noted in PE subjects when compared to control women. Elevation in sFlt-1 and sFlt-1/PlGF ratio was observed at different percentile values in PE cases. The receiver operating characteristic (ROC) area under the curve (AUC) for sFlt-1, PlGF, and sFlt-1/PlGF ratio were 0.869 ± 0.031, 0.463 ± 0.048, and 0.759 ± 0.039, respectively. A systematic shift in sFlt-1, but not in PlGF, distributions for higher values occurred in PE subjects. A progressive increase in the adjusted OR paralleled increased sFlt-1 and the sFlt-1/PlGF ratio percentile values; no similar trend was noted for the PlGF percentiles. Increased sFlt-1 levels and sFlt-1/PlGF ratio were significantly correlated with dysmenorrhea, hypertension, baby weight, and C-section. In contrast, no correlation was found between PlGF and the PE-associated features tested. CONCLUSIONS: Increased sFlt-1 levels and corresponding sFlt-1/PlGF ratio, but not circulating PlGF levels, constitute an independent risk factor for 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".