OC07.03: Identification of women remaining at high risk of pre‐eclampsia after aspirin initiation in the first trimester of pregnancy
Bibliographic record
Abstract
To evaluate the discriminative capacity of uterine artery pulsatility index (UtA-PI) in the identification of women remaining at high risk of pre-eclampsia (PE) after first trimester initiation of aspirin. We conducted a post-hoc analysis of data from a clinical randomised controlled trial of women with prior PE singleton fetus randomised to either 80 mg or 160 mg at bedtime from 10–14 weeks and followed until delivery. A follow-up visit was planned at 16–18 weeks for measurement of mean arterial blood pressure (MAP), UtA-PI and PFA-100 to evaluate aspirin non-responsiveness (PFA-100<145). Mean difference and ROC curves analyses with area under the curve (AUC), detection rate (DR) and false-positive rates (FPR) were used. We observed 14 cases (13%) of PE and 3 (3%) cases of preterm PE out of 106 women with complete follow-up. UtA-PI at 16-18 weeks was discriminative of participants who developed preterm PE (AUC: 0.89; 95%CI: 0.77–1.00) but, not for those who developed term PE (AUC: 0.50; 95%CI: 0.32–0.69). Regarding aspirin resistance, participants who developed PE had a lower PFA-100 (mean difference: 50; 95%CI: 21–79, p=0.001) but the power was insufficient for preterm PE (only 3 cases; mean difference: 37; 95%CI: -48–122, p=0.39). Taking into account aspirin resistance, UtA-PI and MAP, we could identify 62% of PE (at FPR=15%) and 100% of preterm PE (at FPR=6%) at 16-18 weeks. UtA-PI at 16–18 weeks is discriminative of women who will develop preterm PE but not term PE. All women who will develop preterm PE and most of those who will develop term PE despite initiation of aspirin in the first trimester could be identify by the combination of UtA-PI, MAP, and PFA-100 measured at 16–18 weeks.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".