Evidence map›Paper›PMID 40596532›Full record

ArticleScientific reports2025

Development and validation of machine learning models for predicting blastocyst yield in IVF cycles.

Wen-Jie Huo, Fei Peng, Song Quan, Xiao-Cong Wang

Abstract readValidation Study
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Wen-Jie HuoDepartment of Obstetrics and Gynecology, Nanfang Hospital, Southern Medical University, Guangzhou, China.
Fei PengDepartment of Psychology, School of Public Health, Southern Medical University, Guangzhou, China.
Song QuanDepartment of Obstetrics and Gynecology, Nanfang Hospital, Southern Medical University, Guangzhou, China.
Xiao-Cong WangDepartment of Obstetrics and Gynecology, Nanfang Hospital, Southern Medical University, Guangzhou, China. xwang@smu.edu.cn.

Funding

Nanfang Hospital 2023B035National Natural Science Foundation of China 82171656Scientific and Technological Planning Project of Guangzhou City 2023A042302
6 · The paper itself

Abstract

Predicting blastocyst formation poses significant challenges in reproductive medicine and critically influences clinical decision-making regarding extended embryo culture. While previous research has primarily focused on determining whether an IVF cycle can produce at least one blastocyst, less attention has been given to quantifying blastocyst yields. This study aims to develop and validate such a quantitative predictive tool for IVF cycles. We employed three machine learning models-SVM, LightGBM, and XGBoost-which demonstrated comparable performance and outperformed traditional linear regression models (R

Indexed as

BlastocystFertilization in VitroMachine LearningAdultEmbryo Culture TechniquesEmbryonic DevelopmentFemaleHumansPregnancyBlastocyst yieldClinical decision supportExtended embryo cultureIn vitro fertilizationMachine learning

Identifiers

PMID40596532
PMCPMC12217550

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.