ArticleFrontiers in ophthalmology2026
Routine baseline clinical data do not reliably predict poor first-year myopia control with low-dose atropine: a machine-learning benchmarking study.
Article in Frontiers in ophthalmology, 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
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: The aim of this study was to determine whether routinely recorded baseline clinical factors, alone or combined with machine-learning predictive modeling, can provide individual risk stratification for myopia control by identifying children who will progress more than 0.25 diopters (D) during their first year of low-dose atropine treatment. Methods: From a 555-child source cohort treated initially with atropine 0.01% or 0.02%, 468 children had qualifying 9- to 15-month follow-up; 427 children started atropine monotherapy without a baseline optical adjunct. A child was classified as poorly responsive if either treated eye progressed more than 0.25 D of spherical equivalent refraction from baseline to the qualifying visit. This threshold was a practical clinical refraction boundary and was tested in sensitivity analyses using a 0.50-D threshold, annualized progression, and an annualized >0.50 D/year threshold. A prevalence reference, age-plus-refraction logistic regression, elastic-net regression, gradient boosting, and an exploratory multilayer-perceptron neural network were compared by repeated cross-validation. Results: Of 427 children, 159 (37%) were poorly responsive. Discrimination was poor and machine learning provided no improvement: the area under the curve (AUC) was 0.60 for logistic regression and 0.57 for gradient boosting [difference -0.03; 95% confidence interval (CI) -0.06 to 0.01]. Age alone performed similarly (AUC 0.60). No threshold was clinically useful; a high-sensitivity rule flagged 85% of children to identify 88% of poor responders, with positive predictive value 0.39. Conclusions: Routinely recorded baseline factors did not provide clinically useful individual-level prediction of the first-year progression category. With this predictor set, observing early on-treatment progression remains the practical way to identify children who may need escalation.
Indexed as
Identifiers
What OpenQuestion 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 OpenQuestion graph.