In Frozen Embryo Transfer, Is High-Dose Aspirin Better?
Bibliographic record
Abstract
OBJECTIVE: This study aimed to evaluate whether high-dose aspirin during frozen embryo transfer (FET) improves pregnancy outcomes. METHODS: This is a retrospective study of 1207 FET cycles performed in 2022, excluding patients older than 40 years, with recurrent implantation failure, or with recurrent pregnancy loss. Pregnancy outcomes, miscarriage rates, number of clinic visits, and obstetrical outcomes were compared between 2 groups: a group with 81 mg aspirin (January-June 2022) and the other group with 162 mg aspirin (June-December 2022). Aspirin was started on day 1 of the cycle and continued until delivery. Patients were divided into 2 endometrial preparation groups. The modified natural cycle group received ultrasound monitoring, trigger shot at 15 mm follicle size and 7 mm endometrial thickness. The artificial cycle group received estrogen supplementation until endometrial thickness reached ≥7 mm, followed by progesterone. RESULTS: Pregnancy outcomes were similar in both endometrial preparation protocols. The subgroup analysis revealed a trend of lower clinical pregnancy rates and lower live birth rates in the 162 mg aspirin group for both preparation protocols. The only significant complication was hematoma formation, which was higher in the 162 mg group. Multiple regression analysis showed that a higher aspirin dosage and endometrial preparation method significantly increased miscarriage rates. CONCLUSION: High-dose aspirin during FET cycles may negatively impact pregnancy outcomes, increasing miscarriage risk. Lower-dose aspirin (81 mg) may be more beneficial.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".