Risk Reduction In Pregnancy: Evidence-Based Use Of Aspirin For Prevention Of Preeclampsia
Bibliographic record
Abstract
Preeclampsia is a multisystem disorder that emerges in the second half of pregnancy, characterized by the onset of hypertension and end-organ dysfunction. Globally, hypertensive disorders of pregnancy are the second leading cause of direct maternal death.1 Preeclampsia alone it is estimated to affect up to 5% of all pregnancies. This condition poses serious risks to both mother and fetus, including increasing the likelihood of complications such as preterm birth, fetal growth restriction, and long-term cardiovascular consequences. Given the severity of complications associated with preeclampsia, prevention strategies are essential, particularly for individuals at high risk. One of the most well-established interventions is the use of low-dose aspirin, which has been shown to significantly reduce the risk of preeclampsia in select high-risk populations. Leading obstetric organizations, including the Society of Obstetricians and Gynaecologists of Canada (SOGC) and the American College of Obstetricians and Gynecologists (ACOG) have issued guidelines recommending aspirin prophylaxis for individuals with specific risk factors for the development of preeclampsia. Despite this, awareness and implementation of these guidelines vary, highlighting the need for continued education and standardization of care. This article explores the current evidence and guidelines supporting the use of aspirin for preventing preeclampsia. By understanding the benefits of aspirin, identifying appropriate candidates most likely to benefit, and ensuring proper administration, healthcare providers can improve maternal and fetal outcomes and reduce the burden of this serious condition.
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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 | 0.005 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".