Effect of elevated progesterone levels the day before ovulation on pregnancy outcomes in natural cycles of frozen thawed embryo transfer
Bibliographic record
Abstract
We aimed to analyze whether elevated progesterone levels on the day before ovulation affected pregnancy outcomes in natural cycles of frozen thawed embryo transfer (NC-FET). A retrospective analysis was conducted in a public university hospital. Data on clinical pregnancy, live birth, ectopic pregnancy, and miscarriage rates were collected, along with other patient data. Patients were divided into two groups according to their progesterone levels the day before ovulation: the progesterone elevation (PE) group (progesterone level >1.0 ng/mL) and the normal progesterone (NP) group (progesterone level ≤1.0 ng/mL). We assessed the effect of elevated progesterone levels in NC-FET by performing multivariate logistic regression analysis. Overall 1159 women with tubal factor infertility who underwent NC-FET were enrolled, including 666 women who received cleavage-stage embryo transfers and 493 women who received blastocyst embryo transfers. When two cleavage-stage embryos were transferred, the clinical pregnancy rate was significantly higher in the PE than in the NP group following NC-FET (p < .05). After correcting for various confounders, we found that elevated progesterone levels (adjusted odds ratio [OR]: 1.672; 95% confidence interval [CI]: 1.089–2.566, p = .018) improved the clinical pregnancy rate following transfer of two cleavage-stage embryos but did not affect the pregnancy rate when blastocyst-stage embryos were transferred (adjusted OR: 0.856; 95% CI: 0.536–1.369; p = .517). The results showed that in patients undergoing cleavage-stage NC-FET, progesterone levels >1.0 ng/mL improved the clinical pregnancy rates. However, the level of progesterone had no effect on the clinical pregnancy rate for patients undergoing blastocyst-stage NC-FET.
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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 | 0.007 |
| 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) | 0.000 | 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 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".