Evidence map›Paper›PMID 41634697›Full record

ArticleBMC medical informatics and decision making2026

Establishing a prediction model for the therapeutic outcomes of short-term and long-term orthokeratology treatment: using baseline data and changes in AL as dynamic variables.

Zixun Wang, Xiaoxue Hu, Feng Chang, Xiaoling Zhang, Boxuan Sun, Rui Li, Weiping Lin, Ruihua Wei

Abstract read
In one paragraph

Article in BMC medical informatics and decision making, 2026. 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
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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

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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.

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4 · The record

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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.

Zixun Wang *Tianjin Key Laboratory of Retinal Functions and Diseases, Tianjin Branch of National Clinical Research Center for Ocular Disease, Eye Institute and School of Optometry, Tianjin Medical University Eye Hospital, Tianjin, 300384, China.
Xiaoxue Hu *Tianjin Key Laboratory of Retinal Functions and Diseases, Tianjin Branch of National Clinical Research Center for Ocular Disease, Eye Institute and School of Optometry, Tianjin Medical University Eye Hospital, Tianjin, 300384, China.
Feng Chang *Department of Ophthalmology, General Hospital of the Central Theater Command of the People's Liberation Army of China, Wuhan, Hubei, China.
Xiaoling ZhangHandan Eye Hospital (The Third Hospital of Handan), Handan, Hebei, China.
Boxuan SunTianjin Key Laboratory of Retinal Functions and Diseases, Tianjin Branch of National Clinical Research Center for Ocular Disease, Eye Institute and School of Optometry, Tianjin Medical University Eye Hospital, Tianjin, 300384, China.
Rui LiXiaogan Hospital Affiliated to Wuhan University of Science and Technology, Xiaogan, Hubei, China. xgszxyyyklr@126.com.
Weiping LinTianjin Key Laboratory of Retinal Functions and Diseases, Tianjin Branch of National Clinical Research Center for Ocular Disease, Eye Institute and School of Optometry, Tianjin Medical University Eye Hospital, Tianjin, 300384, China. fjctlwp@163.com.
Ruihua WeiTianjin Key Laboratory of Retinal Functions and Diseases, Tianjin Branch of National Clinical Research Center for Ocular Disease, Eye Institute and School of Optometry, Tianjin Medical University Eye Hospital, Tianjin, 300384, China. rwei@tmu.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeTo establish and validate short-term and long-term prediction models for the therapeutic outcomes of orthokeratology (OK) lenses in children, incorporating baseline ocular parameters and dynamic changes in axial length (AL) as predictive variables.

methodsThis retrospective cohort study included 896 pediatric patients who underwent OK lenses treatment at Tianjin Medical University Eye Hospital from January 2020 to July 2025, with a minimum follow-up period of one year. Baseline demographic and ocular parameters were collected, and AL changes were introduced as dynamic predictors in models starting from year two. Seven machine learning algorithms were used to construct prediction models, including LightGBM, random forest, support vector machine (SVM), and artificial neural network (ANN). Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), F1 score, sensitivity, specificity, calibration, and decision curve analyses. Model interpretability was explored using SHAP and LIME analyses.

resultsAmong the 896 included children, 566 achieved reasonable AL control, and 330 showed poor control during the first year. Baseline AL, corneal eccentricity parameters, and refractive error were significantly associated with treatment outcomes. The optimal second-year prediction model (LightGBM) achieved an area under the receiver operating characteristic curve (AUC) of 0.96 with an F1 score of 0.86. For long-term prediction, the SVM model demonstrated moderate and stable performance, with F1 scores of 0.73 and 0.75 in the third- and fourth-year models, respectively.

conclusionPrediction models based on baseline parameters and dynamic AL changes can effectively estimate both short-term and long-term efficacy of OK treatment in children. Dynamic AL change in the first year is a robust predictor of long-term outcomes, offering potential for individualized myopia management strategies.

Indexed as

Axial Length, EyeMyopiaOrthokeratologic ProceduresChildClassification AlgorithmsFemaleHumansMalePrediction AlgorithmsPredictive Learning ModelsRetrospective StudiesTreatment OutcomeAxial lengthMachine learningMyopia controlOrthokeratologyPrediction model

Identifiers

PMID41634697
PMCPMC12998078

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LicenceCC BY-NC-ND
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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.