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Record W4381795490 · doi:10.1093/humrep/dead093.119

O-100 Recurrent implantation failure

2023· article· en· W4381795490 on OpenAlexaff
N.S. Macklon, Danilo Cimadomo, Marco Molina, Georg Griesinger, George T. Lainas, Nathalie Le Clef, David J. McLernon, Debbie Montjean, Bettina Tóth, Nathalie Vermeulen

Bibliographic record

VenueHuman Reproduction · 2023
Typearticle
Languageen
FieldImmunology and Microbiology
TopicReproductive System and Pregnancy
Canadian institutionsOttawa Fertility Centre
Fundersnot available
KeywordsPsychological interventionExpert opinionMedicineAssisted reproductive technologyClinical PracticeMEDLINEPregnancyFamily medicineMedical educationGynecologyInfertilityIntensive care medicineNursingPolitical science

Abstract

fetched live from OpenAlex

Abstract Study question How should Recurrent Implantation Failure (RIF) in ART patients be defined and managed? Summary answer This is the first ESHRE good practice recommendations paper providing a definition for RIF and guidance on how to investigate causes and contributing factors and how to improve the chances of a pregnancy. What is known already RIF is a challenge in the ART Clinic, with a multitude of investigations and interventions offered and applied in clinical practice, often without biological rational or unequivocal evidence of benefit. Study design, size, duration This recommendations document was developed according to a predefined methodology for ESHRE good practice recommendations. Recommendations are supported by data from the literature, if available, the results of a previously published survey on clinical practice in RIF and the expertise of the working group. A literature search was performed in PubMed and Cochrane focusing on “recurrent reproductive failure", "recurrent implantation failure" and "repeated implantation failure“. Participants/materials, setting, methods The ESHRE RIF Working Group included 8 members representing the ESHRE Special Interest Groups of Implantation and Early Pregnancy, Reproductive Endocrinology, and Embryology, and completed with an independent chair and an expert in statistics. The recommendations for clinical practice were formulated based on the expert opinion of the Working Group, while taking into consideration the published data and results of the survey on uptake in clinical practice. The draft document was then opened for online peer review to ESHRE members and revised in light of the comments received. Main results and the role of chance RIF describes the scenario in which the transfer of embryos considered to be viable has failed to result in a positive pregnancy test sufficiently often in a specific patient to warrant consideration of further investigations and/or interventions. The recommended threshold for the cumulative predicted chance of implantation to identify RIF for the purposes of initiating further investigation is 60%. When a couple have not had a successful implantation by a certain number of embryo transfers and the cumulative predicted chance of implantation associated with that number is greater than 60%, then they should be counselled on further investigation and/or treatment options. This term defines clinical RIF for which further actions should be considered. Nineteen recommendations were formulated on investigations when RIF is suspected, and 13 on interventions. Limitations, reason for caution While awaiting the results of further studies and trials, the ESHRE Working group recommends identifying of RIF based on the chance of successful implantation for the individual patient or couple and to restrict investigations and treatments to those supported by a clear rationale and data indicating their likely benefit. Wider implications of the findings This paper provides good practice advise, but also highlights the investigations and interventions that need further research. This research, when well-conducted, will be key to making progress in the clinical management of RIF. Study funding and competing interest(s) Yes. The other authors had nothing to disclose.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.541
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.005

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 head0.037
GPT teacher head0.296
Teacher spread0.260 · 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.

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
Published2023
Admission routes1
Has abstractyes

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