The Potential Impact of Aspirin Prophylaxis on Pregnancy Outcomes for Women With Severe Preeclampsia [37L]
Bibliographic record
Abstract
INTRODUCTION: Preeclampsia affects 3-5% of pregnancies with significant morbidity and mortality. Aspirin may reduce the risk of developing preeclampsia by up to 24%. Several organizations have created guidelines for initiation of aspirin therapy, each using different criteria. This study was designed to assess the impact of differing guidelines for aspirin use on maternal and fetal outcomes. METHODS: A retrospective cohort study of women who delivered between January 2009 and September 2010 with a diagnosis of preeclampsia with severe features was performed. Guidelines from six different OBGYN societies were applied to these patients to identify the percentage who met criteria for aspirin administration. Published risk reduction rates for preeclampsia, intrauterine growth restriction (IUGR), and preterm birth were applied to estimate adjusted maternal and fetal outcomes. RESULTS: Of 153 deliveries with 173 neonates, 31 cases of IUGR (20%) and 107 cases of preterm births (70%) were identified. The Canadian society (SOGC) guideline identified 97% of our patients for aspirin therapy, while the remaining guidelines identified between 4-46% (P<.001) The SOGC was the only guideline to meet the published 24% reduction in cases of preeclampsia and 14% reduction of preterm birth with 35.3% and 14.3% reduction respectively. None of the guidelines reduced IUGR by the published 20% rate. CONCLUSION: The Canadian society’s guideline is comprehensive and captures more women who should start aspirin therapy. While these guidelines are beneficial for initiation of aspirin therapy, few of the published guidelines will effectively reduce rates of preeclampsia. Development of a more effective preventive treatment remains essential.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".