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Record W2980394282 · doi:10.5935/1518-0557.20190054

The KidscoreTM D5 algorithm as an additional tool to morphological assessment and PGT-A in embryo selection: a time-lapse study

2019· article· en· W2980394282 on OpenAlexaboutno aff
Eduardo Gazzo, Fernando Peña, Federico Valdéz, Arturo Chung, Claudio Bonomini, Mario Ascenzo, Marcelo Velit, Ernesto Escudero

Bibliographic record

VenueJBRA · 2019
Typearticle
Languageen
FieldMedicine
TopicPrenatal Screening and Diagnostics
Canadian institutionsnot available
Fundersnot available
KeywordsEmbryoAndrologyPloidySelection (genetic algorithm)PregnancyEmbryo transferBiologyGynecologyMedicineAlgorithmGeneticsComputer scienceMachine learning

Abstract

fetched live from OpenAlex

OBJECTIVE: To evaluate the use of implantation data algorithm KIDscoreTM D5 (Vitrolife®, Canada) as an additional tool for morphological assessment and preimplantation genetic testing for aneuploidies (PGT-A) to improve implantation and ongoing pregnancy rates. MATERIALS AND METHODS: This study looked into 912 embryos from 270 patients who underwent IVF at the INMATER Fertility Clinic in Lima, Peru, between October 2016 and June 2018. All embryos were cultured for up to five or six days in an Embryoscope® time-lapse incubator (Vitrolife®, Canada) and evaluated based on the KIDscoreTM D5 algorithm (KS5). Biopsies for PGT-A screening were performed in 778 (85.31%) embryos. A total of 184 single embryo transfers (68% of patients) were performed during the study period and the embryos transferred were divided into four groups: 1) euploid embryos transferred without consideration to their KS5 scores (n=86); 2) euploid embryos transferred considering their KS5 scores (n=48); 3) embryos transferred without consideration to their KS5 scores and that were not evaluated by PGT-A (n=40); and 4) embryos transferred considering their KS5 scores and that were not evaluated by PGT-A (n=10). Implantation and ongoing pregnancy rates were compared between the groups and between euploid embryos with the highest KS5 scores (KS5=6, n=25) and euploid embryos with the lowest KS5 scores (KS5=1, n=51). The correlations between KS5 scores and embryo euploidy rates were also evaluated. RESULTS: Euploid embryo transfers in which KS5 scores were considered in the selection process had significantly higher Implantation and ongoing pregnancy rates compared to euploid embryo transfers in which selection was based on morphology (75.00% vs. 50.00%; p=0.002 and 66.66% vs. 48.83%; p=0.037, respectively). Additionally, implantation rates were significantly higher for blastocysts with the highest KS5 score (KS5=6) compared to blastocysts with the lowest score (KS5=1) (80.00% vs. 49.02%; p=0.045). Ongoing pregnancy rates were not significantly different (72.00% vs. 47.06%; p=0.105). Euploidy rates were significantly higher in the group of embryos with KS5=6 than in the group of embryos with KS5=1 (61.88% vs. 48.33%; p=0.006). CONCLUSION: Embryo selection based on the KS5 algorithm score improved the implantation rates of single euploid blastocyst transfers. Furthermore, embryos with the highest KS5 score had a higher probability of being euploid and implanting.

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)0.003
metaresearch head score (Gemma)0.006
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 score0.003
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.012
GPT teacher head0.309
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 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

Citations36
Published2019
Admission routes1
Has abstractyes

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