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Record W3190242864 · doi:10.1093/humrep/deab130.366

P–367 A comparison of frozen-thawed embryo transfer protocols in 3,478 frozen embryo transfers

2021· article· en· W3190242864 on OpenAlexaff
V Bellemare, E Kadou. Peero, Ido Feferkorn, William Buckett

Bibliographic record

VenueHuman Reproduction · 2021
Typearticle
Languageen
FieldMedicine
TopicReproductive Biology and Fertility
Canadian institutionsMcGill University
Fundersnot available
KeywordsEmbryo transferSingle Embryo TransferEmbryo cryopreservationEmbryoMedicinePregnancyGynecologyPregnancy rateCryopreservationBlastocystRetrospective cohort studyEmbryo qualityAndrologyOocyteBiologyInternal medicineEmbryogenesis

Abstract

fetched live from OpenAlex

Abstract Study question What frozen-thawed embryo transfer (FET) protocol is associated with the highest live birth rate (LBR)? Summary answer: Natural cycle FET (NC-FET), with or without hCG triggering are associated with higher LBR and clinical pregnancy rate (CPR) compared to artificial HRT-FET cycles. What is known already FET cycles (as opposed to fresh ET) are now the most frequently performed treatment in ART. There are many reasons for this including better laboratory cryopreservation techniques, increased single ET cycles, freeze-all cycles to reduce OHSS, as well as PGT-A and personalized ET. Nevertheless, there is no clear consensus on the most effective protocol. Study design, size, duration Retrospective cohort study with FET of cleavage (n = 220) and blastocyst (n = 3258) embryos thawed 2013–2018 in a single academic center. FET protocols were NC-FET (n = 182), artificial HRT-FET (n = 3159) and modified NC (mNC) with hCG triggering (n = 137). Other cycles (gonadotrophin or GnRH agonist) and women with uterine anomalies were excluded. Primary outcome was LBR. Secondary outcomes were CPR, visits per cycle and endometrial thickness. Adjustment was made for potential known confounders. Participants/materials, setting, methods In NC-FET, no medication was given and ET timing was by serum LH surge. In mNC-FET, hCG was given when the lead follicle reached 18mm rather than awaiting the LH surge. In artificial HRT-FET, estradiol valerate was given and once endometrial thickness reached 8mm, progesterone was added and ET was planned. Adjustment for female age at oocyte retrieval, embryo stage, embryo grade, year of freezing, year of thawing, infertility cause and endometrial thickness was performed. Main results and the role of chance There were no significant differences between the groups with regard to female age at oocyte retrieval, embryo stage, embryo grade, embryo number, cycle number and endometrial thickness. As expected, more women with irregular cycles were included in the artificial HRT-FET compared to NC-FET (16.1% vs. 8.2%, p = 0.003) and mNC-FET (16.1% vs. 4.1%, p < 0.0001). There were more visits per cycle in NC-FET and mNC-FET compared to artificial HRT_FET (p < 0.0001). LBR was higher in the mNC-FET (38.0%) and NC-FET (31.9%) compared to artificial HRT_FET (20.2%) (p = 0.0001 and p = 0.0003 respectively). CPR was higher in mNC-FET compared to artificial HRT-FET (45.3% vs. 32.3%, p = 0.0002), and in NC-FET compared to artificial HRT-FET (44.5% vs. 32.3%, p = 0.0009). There was no significant difference in LBR or CPR between NC-FET and mNC-FET. Sub-analysis of the first FET showed similar results. Biochemical pregnancy loss and miscarriage rates were similar in all groups. The higher LBR with NC-FET and mNC-FET remained significant even after adjusting for potential confounders, (aOR 2.42, 95%CI: 1.53–3.66, p < 0.0001). Limitations, reasons for caution The interpretation of the findings of this study is limited by the retrospective nature of the analysis and the potential for unmeasured confounding variables. Wider implications of the findings: Although artificial HRT FET cycles are more common, convenient and practical for clinicians, with less visits per cycle, its use must be cautiously reconsidered in light of the potential negative effect on LBR when compared with natural cycle FET. Trial registration number Not applicable

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.476
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.074
GPT teacher head0.371
Teacher spread0.297 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2021
Admission routes1
Has abstractyes

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