Prevention of perinatal death and adverse perinatal outcome using low‐dose aspirin: a meta‐analysis
Bibliographic record
Abstract
OBJECTIVE: To compare early vs late administration of low-dose aspirin on the risk of perinatal death and adverse perinatal outcome. METHODS: Databases were searched for keywords related to aspirin and pregnancy. Only randomized controlled trials that evaluated the prophylactic use of low-dose aspirin (50-150 mg/day) during pregnancy were included. The primary outcome combined fetal and neonatal death. Pooled relative risks (RR) with their 95% CIs were compared according to gestational age at initiation of low-dose aspirin (≤ 16 vs > 16 weeks of gestation). RESULTS: Out of 8377 citations, 42 studies (27 222 women) were included. Inclusion criteria were risk factors for pre-eclampsia, including: nulliparity, multiple pregnancy, chronic hypertension, cardiovascular or endocrine disease, prior gestational hypertension or fetal growth restriction, and/or abnormal uterine artery Doppler. When compared with controls, low-dose aspirin started at ≤ 16 weeks' gestation compared with low-dose aspirin started at >16 weeks' gestation was associated with a greater reduction of perinatal death (RR = 0.41 (95% CI, 0.19-0.92) vs 0.93 (95% CI, 0.73-1.19), P = 0.02), pre-eclampsia (RR = 0.47 (95% CI, 0.36-0.62) vs 0.78 (95% CI, 0.61-0.99), P < 0.01), severe pre-eclampsia (RR = 0.18 (95% CI, 0.08-0.41) vs 0.65 (95% CI, 0.40-1.07), P < 0.01), fetal growth restriction (RR = 0.46 (95% CI, 0.33-0.64) vs 0.98 (95% CI, 0.88-1.08), P < 0.001) and preterm birth (RR = 0.35 (95% CI, 0.22-0.57) vs 0.90 (95% CI, 0.83-0.97), P < 0.001). CONCLUSION: Low-dose aspirin initiated at ≤ 16 weeks of gestation is associated with a greater reduction of perinatal death and other adverse perinatal outcomes than when initiated at >16 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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.034 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".