Long-term study of accommodative esotropia.

Purpose: This study aimed, using a large sample size, to determine the long-term results of standard treatment of accommodative esotropia and identify predictors of outcome while minimizing bias in data collection and analysis.

Methods: Data from all the files of a large, long-established pediatric ophthalmology practice were collected and analyzed using a masked protocol. The study included every esotropic patient who had been prescribed glasses.

Long-term study of accommodative esotropia.

Purpose: Previous studies of accommodative esotropia have been hampered by bias-prone methods of data collection and analysis and by small sample size. The studies have conflicting conclusions, causing uncertain results. This study aims to determine long-term results of standard treatment of accommodative esotropia and identify predictors of outcome, while minimizing bias in data collection and analysis, using the largest possible sample size.

Using dependency/association rules to find indications for computed tomography in a head trauma dataset.

Analysis of a clinical head trauma dataset was aided by the use of a new, binary-based data mining technique, termed Boolean analyzer (BA), which finds dependency/association rules. With initial guidance from a domain user or domain expert, the BA algorithm is given one or more metrics to partition the entire dataset. The weighted rules are in the form of Boolean expressions.