Risk Threshold for Starting Low Dose Aspirin in Pregnancy to Prevent Preeclampsia: An Opportunity at a Low Cost
Bibliographic record
Abstract
BACKGROUND: Preeclampsia (PE) increases maternal and perinatal morbidity and mortality. Based on a multitude of data from randomized clinical trials, clinical practice guidelines endorse using ASA to prevent PE in women who are "at risk." However, data are lacking about the level of absolute risk to warrant starting ASA prophylaxis. METHODS AND FINDINGS: We present two approaches for objectively determining the minimum absolute risk for PE at which ASA prophylaxis is justified. The first is a new approach-the minimum control event rate (CERmin). The second approach uses a pre-existing concept-the minimum event rate for treatment (MERT). Here we show how the CERmin is derived, and then use the CERmin and the MERT to guide us to a reasonable risk threshold for starting a woman on ASA prophylaxis against PE based on clinical risk assessment. We suggest that eligible women need not be at "high risk" for preeclampsia to warrant ASA, but rather at some modestly elevated absolute risk of 6-10%. CONCLUSIONS: Given its very low cost, its widespread availability, ease of administration and its safety profile, ASA is a highly attractive agent for the prevention of maternal and perinatal morbidity worldwide.
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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 | 0.011 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".