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 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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".