The role of aspirin and inflammation on reproduction: the EAGeR trial
Bibliographic record
Abstract
Inflammation has been linked to several complications in pregnancy, including pregnancy loss. Due to its anti-inflammatory properties, aspirin, a widely available and inexpensive therapy, has potential to help mitigate the negative effects of inflammation along the reproductive pathway. Therefore, the Effects of Aspirin in Gestation and Reproduction (EAGeR) trial was designed to elucidate whether preconception-initiated daily low-dose aspirin would increase the live birth rate in women with 1-2 prior pregnancy losses and no infertility diagnosis and attempting unassisted conception. Here, we present an overview of the collected findings. Low-dose aspirin was associated with an increased live birth rate among women with a single loss at <20 weeks gestation within the past year. When stratified by tertile of C-reactive protein (CRP), a biomarker of inflammation, treatment with aspirin restored a decrement in the live birth rate in women in the highest CRP tertile (relative risk 1.35, 95% confidence interval 1.08-1.67), increasing to similar rates as women of the lower and mid-CRP tertiles. The same effect modification by inflammation status was observed when examining the effect of low-dose aspirin on offspring sex ratio. These results suggest that inflammation plays an important role in reproduction, and that chronic, low-grade inflammation may be amenable to aspirin treatment.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".