Optimal endometrial thickness in fresh and frozen-thaw in vitro fertilization cycles: an analysis of live birth rates from 96,000 autologous embryo transfers
Bibliographic record
Abstract
OBJECTIVE: To study the effect of increasing endometrial thickness on live birth rates in fresh and frozen-thaw embryo transfer (FET) cycles. DESIGN: Retrospective cohort study. SETTING: National data from Autologous in vitro fertilization (IVF) embryo transfer and FET cycles in Canada from the Canadian Assisted Reproductive Technology Registry Plus (CARTR Plus) database for records between January 2013 and December 2019. PATIENTS: Thirty-three Canadians clinics participated in voluntary reporting of IVF and pregnancy outcomes to the Canadian Assisted Reproductive Technology Registry Plus database, and a total of 43,383 fresh and 53,377 frozen transfers were included. INTERVENTION(S): None. MAIN OUTCOME MEASURE(S): Clinical pregnancy, pregnancy loss, and live birth rates. RESULTS: In fresh IVF-embryo transfer cycles, increasing endometrial thickness is associated with significant increases in the mean number of oocytes retrieved, peak estradiol levels, number of usable embryos, clinical pregnancy rates, live birth rates, and mean term singleton birth weights, and a decrease in pregnancy loss rates. However, live birth rates plateau after 10-12 mm. In contrast, in FET cycles live birth rates plateau after the endometrium measures 7-10 mm. The improvement in live birth rates with increasing endometrial thickness was independent of patient age, timing of embryo transfer (e.g., cleavage stage vs. blastocyst stage), or the number of oocytes at retrieval. CONCLUSIONS: In cycles with a fresh embryo transfer, live birth rates increase significantly until an endometrial thickness of 10-12 mm, while in FET cycles live birth rates plateau after 7-10 mm. However, an endometrial thickness <6 mm was associated clearly with a dramatic reduction in live birth rates in fresh and frozen embryo transfer 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 | 0.007 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 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".