Patterns of Aspirin Non-Use by Preeclampsia Risk Factors in High-Risk Pregnancies: A Retrospective Cohort Study
Bibliographic record
Abstract
OBJECTIVES: Low-dose aspirin (acetylsalicylic acid [ASA]) prevents preterm preeclampsia, yet identifying who should initiate therapy remains challenging. We evaluated patterns of ASA non-use among high-risk pregnant patients with one or more preeclampsia risk factor(s) based on Canadian guidelines in a tertiary obstetrical centre in Ontario, Canada. METHODS: This was a retrospective cohort study of pregnant patients at high risk of placenta-mediated disorders who gave birth at a single tertiary centre from March 1, 2017, to December 31, 2019. We evaluated ASA non-use for individual preeclampsia risk factors and for cumulative risk factors using descriptive analyses and logistic regression. RESULTS: A total of 641 patients were included, 423 (66.0%) of whom did not use ASA. ASA non-use was prevalent among those with prior preeclampsia (33.6%), diabetes (47.1%), and chronic hypertension (45.0%). Risk factors with the highest non-use were nulliparity (76.0%), obesity (59.9%), and age >40 (58.6%). Cumulative risk factors had decreasing odds of non-use compared to no risk factors, although this reduction plateaued with ≥2 risk factors (odds ratio 0.14; 95% CI 0.07-0.27). Among all patients with an ASA indication based on current Canadian guidelines, 55.3% were not using ASA during their pregnancy. CONCLUSIONS: ASA non-use rates remain high among patients with significant risk factors for preeclampsia, including prior preeclampsia, diabetes, hypertension, obesity, and nulliparity. The presence of multiple risk factors is associated with minimal improvement in ASA use rates. Strategies are urgently needed to improve ASA use for preeclampsia prevention among high-risk pregnant patients.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".