Low-Dose Aspirin in Early Gestation for Prevention of Preeclampsia and Small-for-Gestational-Age Neonates: Meta-analysis of Large Randomized Trials
Bibliographic record
Abstract
Objectives Meta-analyses of small to moderate size randomized controlled trials (RCTs) suggested that aspirin started before 17 weeks' gestation reduces the risk of preeclampsia and small-for-gestational-age (SGA) neonates. We evaluated data from large randomized trials originally excluded from meta-analyses. Methods We performed meta-analyses of RCTs including more than 350 participants that compared aspirin to placebo during pregnancy. Corresponding authors were contacted to obtain data according to gestational age. Outcomes included preeclampsia, severe preeclampsia, and SGA. Relative risks (RRs) with their 95% confidence intervals (CIs) were calculated. Results Data for women recruited before 17 weeks' gestation were obtained for three (50%) of the six eligible trials for a total of 11,949 participants including 3,293 recruited before 17 weeks' gestation with available data. We observed no impact of low-dose aspirin (60 mg) started before 17 weeks' gestation on the risk of preeclampsia (RR: 0.93; 95% CI: 0.75-1.15), severe preeclampsia (RR: 0.96; 95% CI: 0.71-1.28), or SGA (RR: 0.84; 95% CI: 0.56-1.26) and it was not statistically different than when started at or after 17 weeks' gestation. Conclusion Data from large randomized trials do not support greater benefits of low-dose aspirin (at 60 mg daily) when started before 17 weeks' gestation for the prevention of preeclampsia or SGA.
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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 | 0.026 | 0.056 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.045 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".