Aspirin at 75 to 81 mg Daily for the Prevention of Preterm Pre-Eclampsia: Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: Aspirin at 150 mg daily, initiated in the 1st trimester of pregnancy, prevents preterm pre-eclampsia. We aimed to estimate whether a dose of 75 to 81 mg daily can help to prevent preterm pre-eclampsia as well. Methods: A systematic search was conducted using multiple databases and meta-analyses of randomized controlled trials (RCTs) that compared aspirin initiated in the first trimester of pregnancy to placebo or no treatment, following the PRISMA guidelines and the Cochrane risk of bias tool. Results: We retrieved 11 RCTs involving 13,981 participants. Five RCTs had a low risk of bias, one at unclear risk, and fiver had a high risk of bias. A pooled analysis demonstrated that doses of 75 to 81 mg of aspirin, compared to a placebo or no treatment, was not associated with a significant reduction in preterm pre-eclampsia (8 studies; 12,391 participants; relative risk, 0.66; 95% confidence interval: 0.27 to 1.62; p = 0.36), but there was a significant heterogeneity across the studies (I2 = 61%, p = 0.02). Conclusion: It cannot be concluded that taking 75 to 81 mg of aspirin daily reduces the risk of preterm pre-eclampsia. However, given the significant heterogeneity between the studies, the true effect that such a dose of aspirin would have on pregnancy outcomes could not be properly estimated.
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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 | 0.010 | 0.032 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.031 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".