Evidence map›Paper›PMID 41099009›Full record

ArticleChina CDC weekly2025

Predicting Cycloplegic Spherical Equivalent Refraction Among Children and Adolescents Using Non-cycloplegic Data and Machine Learning - China, 2020-2024.

Keke Liu, Ran Qin, Huijuan Luo, Huining Kuang, Ranbo E, Chenyu Zhang, Bingjie Sun, Xin Guo

Abstract read
In one paragraph

Article in China CDC weekly, 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. The application of artificial intelligence in myopia prediction for children and adolescents: a review.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2026
    Review
  2. Article
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

8 authors.

Keke Liu *School of Public Health, Capital Medical University, Beijing, China.
Ran Qin *Beijing Center for Disease Control and Prevention, Beijing, China.
Huijuan LuoBeijing Center for Disease Control and Prevention, Beijing, China.
Huining KuangSchool of Public Health, Capital Medical University, Beijing, China.
Ranbo ESchool of Public Health, Capital Medical University, Beijing, China.
Chenyu ZhangSchool of Public Health, Capital Medical University, Beijing, China.
Bingjie SunBeijing Center for Disease Control and Prevention, Beijing, China.
Xin GuoSchool of Public Health, Capital Medical University, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Cycloplegic refraction is the gold standard for assessing refractive error in children. However, logistical constraints hinder its implementation in large-scale surveys. Methods: Data obtained from a nationwide ocular health survey conducted in ten provincial-level administrative divisions in China were analyzed (2020-2024). Participants aged 5-18 years underwent standardized non-cycloplegic and cycloplegic autorefraction, axial length (AL), corneal radius (CR), and AL/CR measurements. Random forest and XGBoost models were trained to predict the cycloplegic spherical equivalent (SE) using non-cycloplegic SE, uncorrected visual acuity (UCVA), and biometric parameters. Performance was evaluated using R Results: Both models exhibited strong predictive performance. In the test set, random forest achieved R Conclusion: Machine learning models incorporating noncycloplegic SE and ocular biometrics accurately estimate cycloplegic SE in children and adolescents, providing a practical alternative for large-scale refractive-error surveillance when cycloplegia is impractical.

Indexed as

Cycloplegic refractionmachine learningnon-cycloplegic predictionspherical equivalent

Identifiers

PMID41099009
PMCPMC12518965

What OpenQuestion holds

Textmetadata
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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.