Does high serum oestradiol during stimulation influence obstetrical outcomes and placental pathology in subsequent frozen embryo transfers?
Bibliographic record
Abstract
AIM: High oestradiol levels during in vitro fertilization (IVF) fresh cycles have been linked to adverse obstetric outcomes, yet whether this is due to endometrial or oocyte effects remains unclear. Investigating subsequent frozen embryo transfer (FET) cycles can help clarify the origins of these effects. This study aimed to evaluate obstetric outcomes and placental histology in FET cycles for patients with elevated serum oestradiol levels during the ovarian stimulation cycle in which the embryos were created. METHODS: A single centre retrospective cohort study of live singleton deliveries after IVF with programmed FET from 2009 to 2017. High oestradiol during ovarian stimulation was defined as ≥10,000 pmol/L. We compared obstetric outcomes and placental findings between pregnancies with high oestradiol levels in the preceding ovarian stimulation cycle and a control group. RESULTS: We analyzed 114 deliveries in the high oestradiol group and 194 in the control group. Baseline demographics were comparable between groups. No significant differences were observed in obstetric outcomes, including low birth weight, preeclampsia and preterm delivery. The placental macroscopic and histopathological findings did not significantly differ between the groups as well. CONCLUSION: High oestradiol during the ovarian stimulation cycle used to create embryos is not associated with adverse obstetric outcomes or placental pathologies in pregnancies following FET. This is consistent with an endometrial effect of high oestradiol and thus support the practice of a freeze all approach in high oestradiol cycles.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".