Meta-analysis of low-molecular-weight heparin to prevent recurrent placenta-mediated pregnancy complications
Bibliographic record
Abstract
A 35-year-old woman with recurrent severe placenta-mediated pregnancy complications in her 2 pregnancies asks: Will low-molecular-weight heparin help prevent recurrent placenta-mediated pregnancy complications in my next pregnancy? We performed a meta-analysis of randomized controlled trials (RCTs) comparing low-molecular-weight heparin (LMWH) vs no LMWH for the prevention of recurrent placenta-mediated pregnancy complications. We identified six RCTs that included a total of 848 pregnant women with prior placenta-mediated pregnancy complications. The primary outcome was a composite of pre-eclampsia (PE), birth of a small-for-gestational-age (SGA) newborn (<10th percentile), placental abruption, or pregnancy loss >20 weeks. Overall, 67 (18.7%) of 358 of women being given prophylactic LMWH had recurrent severe placenta-mediated pregnancy complications compared with 127 (42.9%) of 296 women with no LMWH (relative risk reduction, 0.52; 95% CI, 0.32 to 0.86; P = .01; I(2), 69%, indicating moderate heterogeneity). We identified similar relative risk reductions with LMWH for individual outcomes, including any PE, severe PE, SGA <10th percentile, SGA <5th percentile, preterm delivery <37 weeks, and preterm delivery <34 weeks with minimal heterogeneity. LMWH may be a promising therapy for recurrent, especially severe, placenta-mediated pregnancy complications, but further research is required.
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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 | 0.012 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.038 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".