The science of frozen embryo transfer, is modified natural cycle better?
Bibliographic record
Abstract
The aim of this study was to compare pregnancy, obstetrical outcomes and number of visits between patients undergoing frozen embryo transfer in artificial vs modified natural cycle. A total of 1207 frozen single embryo transfer cycles performed in 2022 were retrospectively studied. Patients older than 40, with recurrent implantation failure, and recurrent pregnancy loss were excluded. Patients were divided according to their age, BMI, AMH, and type of embryo transfer protocol. Patients in the modified natural cycle group were followed by ultrasound until triggering criteria met, then HCG trigger was scheduled, and the embryo transferred 7 days later. In the artificial cycle group, patients received estrogen supplementation after downregulation, and when the endometrium reached a thickness ≥ 7 mm an embryo transfer was scheduled following intramuscular progesterone administration for 5 days. A total of 649 patients were included in the study. A higher percentage of patients in the artificial cycle group had an initial positive B-hCG test result. The modified natural group had significantly better clinical pregnancy and live birth rates, mainly due to the significantly higher miscarriage rate observed in the artificial cycle group. There was no difference in the mean endometrial thickness between both groups. The number of visits was higher in the m-NC group. Patients with a m-NC protocol had a lower risk of hypertensive disorders of pregnancy (HDP), but a higher risk of gestational diabetes, though the results were non-significant. In conclusion embryo transfer in m-NC yielded a higher live birth rate, more frequent clinic visits, and lower chances of miscarriage.
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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 | 0.002 | 0.010 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".