The relationship between serum estradiol and progesterone levels one day before frozen embryo transfer and pregnancy rates in artificially prepared frozen embryo cycles: are there any threshold serum hormone levels to predict pregnancy in luteal support by the vaginal and subcutaneous route combined?
Bibliographic record
Abstract
Objective: . Material and Methods: levels on the day before FET in 167 cases prepared with hormone replacement therapy between February 2022 and October 2023. The primary outcomes assessed were the pregnancy and live birth rates. We modeled a cut-off serum value based on luteal support for pregnancy. Luteal support was through a combination of vaginal suppositories and subcutaneous injections. Multivariate logistic regression was used to test relationships between pregnancy outcomes and independent variables. Cut-off values were evaluated using receiver operating characteristic (ROC) analysis and percentile analysis. Results: levels conceived (range 10.0-15.6 ng/mL). When multivariate logistic regression was used, this finding lost significance suggesting that low serum levels are related to age, BMI, and/or other factors. Conclusion: is used for programmed FET. Although this finding may be due to the confounding effects of age, BMI, and other factors affecting steroid metabolism, when controlled for in the multivariate logistic regression.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".