<scp>OP</scp>25.01: Heparin added to aspirin for the prevention of pre‐eclampsia: a systematic review and meta‐analysis
Bibliographic record
Abstract
In several countries, it is common practice to use low-molecular weight heparin (LMWH) for the prevention of recurrent adverse placenta-mediated outcomes of pregnancy. We aimed to estimate the impact of adding heparin to low-dose aspirin on the risk of pre-eclampsia and other adverse obstetrical outcomes. A systematic review and meta-analysis of randomized controlled trials were performed. Women randomized to any type of heparin (LMWH or unfractionated heparin) in addition to low-dose aspirin were compared to those who received only low-dose aspirin at or before 16 weeks'. The outcomes of interest were pre-eclampsia, severe pre-eclampsia, fetal growth restriction and livebirth. Pooled relative risks (RR) with their 95% confidence intervals (CI) were calculated. Out of 1638 citations, ten met the inclusion criteria including five that reported the risk of pre-eclampsia. LMWH added to aspirin (75 - 100 mg) in early pregnancy was associated with a significant reduction of the risk of pre-eclampsia (RR 0.54, 95% CI 0.32 to 0.91, p = 0.02), severe pre-eclampsia (RR 0.16, 95% CI 0.03 to 0.88, p = 0.03), fetal growth restriction (RR 0.56, 95% CI 0.32 to 0.97, p = 0.04) in trials including mainly women with prior history of adverse placenta-mediated outcomes. Finally, we observed a positive effect of LMWH or unfractionated heparin added to aspirin on the rate of livebirth (RR 1.07 95% CI 1.00 to 1.14, p = 0.04) compared to aspirin alone. The actual evidence suggests that the addition of LMWH to 75–100 mg of aspirin daily reduces the rate of pre-eclampsia, but mainly severe pre-eclampsia and other placenta-mediated outcomes in women with similar prior history. Since one third of pregnant women are “aspirin resistant” at 75–100 mg, future trials should evaluate the benefits of LMWH to higher dosage (150–160 mg) of aspirin.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.030 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".