Improved Early Prediction of Preterm Pre-Eclampsia by Combining Second Trimester Maternal Serum Alpha-Fetoprotein and Uterine Artery Doppler
Bibliographic record
Abstract
Background: Pre-eclampsia (PE) is a leading cause of maternal and perinatal morbidity and mortality. One of the primary aims of antenatal care is to identify women at high risk and provide them with prophylactic treatment and more intensive surveillance. Current identification is based mainly on maternal characteristics, which is not specific and sensitive enough to be an ideal screening test highlighting the need for an alternative. We evaluated the combination of second trimester maternal serum alpha-fetoprotein (MSAFP) and uterine artery Doppler (UAD) studies for the prediction of PE. Methods: A total of 724 women had MSAFP and UAD measured. The presence of notches and Resistance Index were measured. ROC ’ s were created for MSAFP and UAD alone and in combination. Sensitivities for the two outcomes were compared for a fixed specificity of 94% for PE and 97% for preterm PE. Results: A total of 41 women (5.7%) developed PE. The sensitivity of using UAD (bilateral notches/mean RI ≥ 0.735) was 60.9% and for MSAFP was 24.4% ( ≥ 2.0 MoM). The combination of UAD (bilateral notches/mean RI ≥ 0.55) and MSAFP ( ≥ 1.2 MoM), didn ’ t improve the sensitivity of UAD for PE for the same specificity; 17 women (2.4%) developed preterm PE. The sensitivity using UAD (bilateral notches/mean RI ≥ 0.75) was 29.4% and for MSAFP ( ≥ 2.6 MoM) was 5.9%. The combination of UAD (bilateral notches/mean RI ≥ 0.55) and MSAFP ( ≥ 1.6 MoM), improved the sensitivity for preterm preeclampsia to 64.7% (OR 52.17 (CI 17.81 - 152.84)). The improvement in sensitivity for the combined method was statistically significant compared to MSAFP (P < 0.01) or UAD (P < 0.02) alone. J Clin Gynecol Obstet. 2014;3(1):22-29 doi: http://dx.doi.org/10.14740/jcgo223w
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".