OP01.03: Comparison of 80 versus 160 mg of aspirin in pregnant women with previous history of pre‐eclampsia: a randomised controlled trial
Bibliographic record
Abstract
Impaired placentation is a main contributor for pre-eclampsia (PE). Low-dose aspirin (50–162 mg daily) reduces the risk of placenta-mediated complications of pregnancy including PE when started in early pregnancy. We aimed to evaluate whether the dose of aspirin influences its impact on placentation. A double-blinded randomised controlled trial (RCT) including pregnant women with singleton pregnancy at 10–14 weeks of gestation with a previous history of pre-eclampsia. Women were randomly assigned to take 80 mg or 160 mg of aspirin each evening. Placental function was assessed by Doppler ultrasound (uterine artery pulsatility index) and biochemistry (sFlt-1, PlGF and endoglin) at 15–18, 20–23 (primary outcome) and 32–35 weeks of gestation. 107 women were recruited at a mean gestational age (GA) of 12.70.8 weeks; 53 were randomly allocated to the group 80 mg and 54 to the group 160 mg. Follow-up was achieved in 107 (100%) women at 17.0 ± 0.6 weeks; 104 (97%) at 22.8 ± 0.8 weeks; and 102 (95%) at 33.4 ± 0.8 weeks. We observed a strong compliance (>95%) to the treatment in both groups. We observed no significant difference between the two groups in terms of UtA PI and biochemical markers at each subsequent visit (primary outcome: UtA PI at 22–23 weeks: 0.97 ± 0.30 vs. 0.98 ± 0.34, p = 0.90). When taken in the evening with excellent compliance, there is no difference between 80 vs. 160 mg of aspirin in terms of placental function evaluated in the second and third trimester of pregnancy.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".