ArticleJournal of AAPOS : the official publication of the American Association for Pediatric Ophthalmology and Strabismus2025
Using machine learning to identify pediatric ophthalmologists.
Article in Journal of AAPOS : the official publication of the American Association for Pediatric Ophthalmology and Strabismus, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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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
1 citing paper in PubMed.
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
This cross-sectional study used data from the American Academy of Ophthalmology IRIS Registry (Intelligent Research in Sight) and machine learning algorithms to identify pediatric ophthalmologists based on physician coding patterns. A random forest model achieved an area under the receiver operating characteristic curve of 0.98, sensitivity of 0.98, and specificity of 0.88 when classifying pediatric eye specialists in the test validation cohort. Algorithm-based approaches to identify pediatric ophthalmologists using procedure codes may offer new avenues to determine the scope, scale, and trajectory of pediatric eye care delivery.
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Registered trials
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