Aspirin for preeclampsia prevention in low- and middle-income countries: mind the gaps
Bibliographic record
Abstract
Preeclampsia is a syndrome that continues to be a major contributor to maternal and neonatal mortality, especially in low-income countries. Low-dose aspirin reduces the risk of preeclampsia, but the mechanism is still unknown. Risk factors to identify women at risk of preeclampsia are based on clinical characteristics. Women identified as high-risk would benefit from aspirin treatment initiated, preferably at the end of the first trimester. Current efforts have largely focused on developing screening algorithms that incorporate clinical risk factors, maternal biomarkers, and uterine artery Doppler evaluated in the first trimester. However, most studies on preeclampsia are conducted in high-income settings, raising uncertainties about whether the information gained can be totally applied in low-resource settings. In low- and middle-income countries, lack of adequate antenatal care and late commencement of antenatal care visits pose significant challenges for both screening for preeclampsia and initiating aspirin treatment. Furthermore, the preventive effect of first-trimester screening based on algorithms and subsequent aspirin treatment is primarily seen for preterm preeclampsia, and reviews indicate minimal or no impact on reducing the risk of term preeclampsia. The lack of evidence regarding the effectiveness of aspirin in preventing term preeclampsia is a crucial concern, as 75% of women will develop this subtype of the syndrome. Regarding adverse outcomes, low-dose aspirin has been linked to a possible higher risk of postpartum hemorrhage, a condition as deadly as preeclampsia in many low- and middle-income countries. The increased risk of postpartum hemorrhage among women in low-income settings should be taken into consideration when discussing which pregnant women would benefit from the use of aspirin and the ideal aspirin dosage for preventing preeclampsia. In addition, women's adherence to aspirin during pregnancy is crucial for determining its effectiveness and complications, an aspect often overlooked in trials. In this review, we analyze the knowledge gaps that must be addressed to safely increase low-dose aspirin use in low- and middle-income countries, and we propose directions for future research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".