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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".