Evidence map›Paper›PMID 40745285›Full record

ArticleBMC ophthalmology2025

Development and validation of a model for predicting myopia in young children in China.

Ting Hu, Rong Wu, Xi He, Zi-Yu Hua, Tian-Li Peng, Xiao-Yan Peng, Shi-Ming Li

Abstract readValidation Study
In one paragraph

Article in BMC ophthalmology, 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

7 authors.

Ting HuBeijing Yanqing District Hospital (Peking University Third Hospital Yanqing Hospital), Yanqing District, Beijing, China.
Rong WuBeijing Yanqing District Hospital (Peking University Third Hospital Yanqing Hospital), Yanqing District, Beijing, China.
Xi HeDepartment of Ophthalmology, Beijing Tongren Hospital, Capital Medical University, Beijing, China.
Zi-Yu HuaDepartment of Ophthalmology, Beijing Tongren Hospital, Capital Medical University, Beijing, China.
Tian-Li PengDepartment of Ophthalmology, Beijing Tongren Hospital, Capital Medical University, Beijing, China.
Xiao-Yan Peng *Department of Ophthalmology, Beijing Tongren Hospital, Capital Medical University, Beijing, China. 74000041@ccmu.edu.cn.
Shi-Ming Li *Department of Ophthalmology, Beijing Tongren Hospital, Capital Medical University, Beijing, China. lishiming81@163.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundEarly detection of myopia risk and timely intervention are key to reducing the incidence of myopia. This study aimed to construct a myopia prediction model that included perinatal factors and evaluate the correlation between each factor and myopia.

methodsA cross-sectional study was conducted. A questionnaire was used to investigate the related factors. The predictors of myopia onset were determined via univariate analysis (Model A), LASSO regression and 10-fold cross validation (Model B). The perinatal factors in Model B were removed to form Model C. Binary logistic regression was used to establish the model. Then, internal and external verification were performed. The differentiation, calibration, and clinical benefits of the three models were compared.

resultsA total of 1322 participants with an average age of 7.85 ± 1.58 years were included in the study. Model B included age, axial length (AL), AL to corneal curvature (AL/CR) ratio, steep K (K2) value, parental myopia, and maternal history of gestational hypertension. Neither Model A nor Model C included perinatal factors. Model B had the best discrimination ability in the development, internal validation, and external validation groups and showed the best consistency and clinical benefit in all three groups. After model standardization, the effects of the respective variables on the results were as follows: AL/CR ratio: 59.14, maternal history of gestational hypertension: 44.03, AL: 30.47, K2: 5.65, parental myopia: 4.55, and age: 0.35.

conclusionsA maternal history of gestational hypertension was associated with myopia. The effect of perinatal factors on myopia deserves attention.

trial registrationChiCTR2200065398. November 3, 2022. ( http://www.chictr.org.cn ).

Indexed as

MyopiaRefraction, OcularAxial Length, EyeChildChild, PreschoolChinaCross-Sectional StudiesFemaleHumansIncidenceMalePrediction AlgorithmsRisk FactorsBiometryGestational hypertensionMyopia onsetPrediction model

Identifiers

PMID40745285
PMCPMC12312385

What OpenQuestion holds

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Registered trials

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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.