The Medical Management of Antiphospholipid Syndrome in Pregnancy
Bibliographic record
Abstract
INTRODUCTION: Controversies exist regarding the optimal medical management of antiphospholipid syndrome in pregnancy to prevent obstetric complications. We therefore conducted a systematic review and meta-analysis to evaluate the effect of different pharmacotherapies on pregnancy outcomes in women with antiphospholipid syndrome. METHODS: We searched the Cochrane Library, EMBASE, and MEDLINE from inception to June 2013. Randomized controlled trials (RCTs) examining the use of pharmacotherapies (aspirin, low-molecular weight heparin [LMWH], unfractionated heparin [UFH], intravenous immunoglobulin) in pregnant women with antiphospholipid syndrome were included. Data were extracted independently by two reviewers. Live birth data were pooled across RCTs using a random-effects model. RESULTS: Sixteen RCTs investigating pregnancy outcomes in women with antiphospholipid syndrome (n=803) were included in our meta-analysis. When data were pooled across RCTs, the combination of aspirin and UFH increased live birth rates compared with aspirin alone (relative risk [RR] 1.54, 95% confidence interval [CI] 1.25–1.89), whereas the combination of aspirin and LMWH resulted in a similar rate as aspirin alone (RR=1.07; 95% CI=0.88; 1.29). However, the combination of aspirin and LMWH did increase live births compared to IVIG (RR 1.64, 95% CI 1.21–2.22). The results of other pairwise comparisons were not statistically significant, although some estimates were accompanied by wide 95% CIs and did not rule out clinically important differences (Fig. 1).CONCLUSIONS: Our meta-analysis suggests that the combination of aspirin and UFH results in a higher live birth rate than aspirin alone, whereas the combination of aspirin and LMWH was superior to intravenous immune globulin.
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 | 0.011 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.010 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".