ArticleFrontiers in public health2026
Assessing the reliability of non-cycloplegic refraction in children: a machine learning approach based on non-cycloplegic parameters.
Article in Frontiers in public health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
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.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Traditional cycloplegic refraction is the gold standard for pediatric vision screening but is often limited by low efficiency and poor compliance. This study aimed to develop a machine learning model using non-cycloplegic visual function and refractive parameters to evaluate the reliability of non-cycloplegic refraction in children and adolescents. The model aims to identify individuals at risk of significant refractive error deviation (defined as an absolute difference in spherical equivalent [DSE] > 0.25 D) and to determine the necessity for cycloplegic intervention. Methods: A total of 300 children and adolescents (547 eyes) were included. Eighteen demographic, refractive, and binocular vision variables were collected. Feature selection was performed using the univariate logistic regression and least absolute shrinkage and selection operator (LASSO). Five models (decision tree, logistic regression, support vector machine, random forest, and multilayer perceptron) were developed. Performance was evaluated using accuracy, sensitivity, specificity, area under the receiver operating characteristic curve (AUC), and decision curve analysis (DCA), and interpretability was assessed using SHapley Additive exPlanations (SHAP). Results: An absolute DSE greater than 0.25 D was associated with multiple accommodative and refractive parameters. Logistic regression showed the best performance, with an AUC of 0.871 (95% CI: 0.798-0.944), an accuracy of 0.809, a sensitivity of 0.771, and a specificity of 0.827. The most important predictors included the monocular estimation method (MEM), negative relative accommodation (NRA), positive relative accommodation (PRA), cylindrical, and accommodative facility. A nomogram was constructed to estimate the probability of DSE greater than 0.25 D. Conclusion: Machine learning models based on routine non-cycloplegic parameters can effectively identify children who require cycloplegic refraction, providing an interpretable and practical decision-support tool to reduce unnecessary cycloplegia and improve clinical efficiency.
Indexed as
Identifiers
What Socratic holds
Registered trials
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 Socratic graph.