Prediction of Preeclampsia in Twins Using First Trimester: cffDNA Fraction, PlGF, and MAP
Bibliographic record
Abstract
OBJECTIVES: To evaluate cell-free fetal DNA fraction (cffDNAF) as a first-trimester screening marker for preeclampsia necessitating delivery before 37 weeks' gestation in twin pregnancies alone and combined with other bio-markers. METHODS: Women with two live fetuses were enrolled in the first trimester, and evaluated for cffDNAF as a first trimester preeclampsia marker alone, and with placental growth factor (PlGF), mean arterial pressure (MAP), and uterine artery pulsatility index (UtA-PI). RESULTS: There were 20 affected women; the cffDNAF was 9.0% (IQR: 8.4%-10.3%) in the affected, compared to 14% (IQR: 11%-16%) in 163 unaffected cases (p < 0.001). The AUROC for cffDNAF was 0.73 (95% CI: 0.61-0.85, p < 0.001), PlGF had an AUROC of 0.71 (0.59-0.83, p = 0.001), MAP had AUROC of 0.61 (0.50-0.72, p = 0.053) whereas UtA-PI had AUROC of 0.54 (0.39-0.69, p > 0.05). Combining all three biomarkers yielded an AUROC of 0.89 (0.78-0.98), with a sensitivity of 81%, specificity of 90%, negative predictive value (NPV) of 97.5%, and positive predictive value (PPV) of 50.7 UtA-PI did not contribute to the AUROC. CONCLUSION: In twin pregnancies low first trimester cffDNAF effectively screens for preeclampsia necessitating delivery before 37 weeks' gestation, which is augmented with PlGF and MAP.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".