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Record W4283749902 · doi:10.1093/humrep/deac107.277

P-289 KIDscore and PGT-A: Is there a relationship between the findings?

2022· article· en· W4283749902 on OpenAlexaboutno aff
R Azambuja, F Mariani Wingert, LF Proença, Marta Ribeiro Hentschke, I Badalotti-Teloken, Victória Campos Dornelles, Álvaro Petracco, Mariangela Badalotti

Bibliographic record

VenueHuman Reproduction · 2022
Typearticle
Languageen
FieldMedicine
TopicPrenatal Screening and Diagnostics
Canadian institutionsnot available
Fundersnot available
KeywordsEmbryoBlastocystEmbryo transferAndrologyPloidyReproductionBiologyAnalysis of varianceGynecologyMedicineEmbryogenesisGeneticsInternal medicine

Abstract

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Abstract Study question Is there a correlation between Preimplantation Genetic Tests (PGT) results and Embryoscope’s KIDscore? Summary answer It seems that the higher the KIDscore, the higher the percentage of euploid embryos. What is known already Time lapse technology is bringing new perspectives in the relationship of embryos' morphokinetics and implantation rates after assisted reproduction techniques. However, it seems that only the embryo morphokinetics could be insufficient to predict euploidy. The KIDscoreTM D5 (KS5) algorithm, thus, is used for improving the implantation rates after a single euploid embryo transfer in its blastocyst stage and is related to higher rates of euploid embryos the higher the KS5, which could lead to higher implantation rates. Study design, size, duration Retrospective, observational study performed at a reproductive medicine center, using data collected between 2019 and 2021. A total of 802 embryos were included for analysis. Participants/materials, setting, methods All the embryos were biopsied for PGT (A, SR, and M), after being cultured for five or six days in an Embryoscope® time-lapse incubator (Vitrolife®, Canada). The embryos were then divided into three groups according to the KS5 evaluation: G1 (1-4), G2 (4.1-7), G3 (7.1-9.9) and the percentage of euploidy was evaluated in each group. For statistical analysis, Chi-square, and ANOVA tests, and Pearson correlation were used, considering p < 0.05. Main results and the role of chance The women’s mean age among groups G1 vs. G2 vs. G3 was, respectively: 39.1±3.5 vs. 38.7±3.3 vs. 37.6±3.8, p < 0.001; the mean KS5 of each group was: 2.9±0.7 vs. 5.4±0.8 vs. 8.0±0.7, p < 0.001 and finally, the euploidy rates comparing the G1 vs. G2 vs. G3 were, respectively: 98/341, 28.7% vs. 124/340, 36.5% vs. 63/121, 52.1%, p < 0.001. A weak correlation between women's age and KIDScore was also observed (-0.173, p < 0.001). Limitations, reasons for caution Although there is a positive correlation between embryomorphokinetics and euploidy, and the euploidy rate increases with higher KIDscore, only 50% of the high score embryos are euploids. Therefore, it is still important to perform embryo biopsy. Wider implications of the findings The findings suggest that better embryo morphokinetics provide greater chances of euploidy. Moreover, a weak negative correlation between women's age and KIDScore, possibly due to age-related aneuploidy, was observed. These results highlight time-lapse technology’s importance and the future perspective of morphokinetics evaluation improving implantation rates through euploidy identification. 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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0130.001

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 head0.080
GPT teacher head0.313
Teacher spread0.233 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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
Published2022
Admission routes1
Has abstractyes

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