No abstract

OBJECTIVEChiari malformation Type I (CM-I) is typically defined on imaging by a cerebellar tonsil position ≥ 5 mm below the foramen magnum. Low cerebellar tonsil position is a frequent incidental finding on brain or cervical spine imaging, even in asymptomatic individuals. Nonspecific symptoms (e.g., headache and neck pain) are common in those with low tonsil position as well as in those with normal tonsil position, leading to uncertainty regarding appropriate management for many patients with low tonsil position and nonspecific symptoms. Because cerebellar tonsil position is not strictly correlated with the presence of typical CM-I symptoms, the authors sought to determine if other 2D morphometric or 3D volumetric measurements on MRI could distinguish between patients with asymptomatic and symptomatic CM-I.METHODSThe authors retrospectively analyzed records of 102 pediatric patients whose records were in the University of Michigan clinical CM-I database. All patients in this database had cerebellar tonsil position ≥ 5 mm below the foramen magnum. Fifty-one symptomatic and 51 asymptomatic patients were matched for age at diagnosis, sex, tonsil position, and tonsil morphology. National Institutes of Health ImageJ software was used to obtain six 2D anatomical MRI measurements, and a semiautomated segmentation tool was used to obtain four 3D volumetric measurements of the posterior fossa and CSF subvolumes on MRI.RESULTSNo significant differences were observed between patients with symptomatic and asymptomatic CM-I related to tentorium length (50.3 vs 51.0 mm; p = 0.537), supraoccipital length (39.4 vs 42.6 mm; p = 0.055), clivus-tentorium distance (52.0 vs 52.1 mm; p = 0.964), clivus-torcula distance (81.5 vs 83.3 mm; p = 0.257), total posterior fossa volume (PFV; 183.4 vs 190.6 ml; p = 0.250), caudal PFV (152.5 vs 159.8 ml; p = 0.256), fourth ventricle volume to caudal PFV ratio (0.0140 vs 0.0136; p = 0.649), or CSF volume to caudal PFV ratio (0.071 vs 0.061; p = 0.138).CONCLUSIONSNo clinically useful 2D or 3D measurements were identified that could reliably distinguish pediatric patients with symptoms attributable to CM-I from those with asymptomatic CM-I.

a b s t r a c t Available online xxxxPurpose: Pediatric obesity is a serious health problem affecting 1 of 6 children in the U.S. A two-to threefold increased incidence is evident in the last two decades. This study analyzes the outcomes of the ProActive Kids Foundation three-tiered early intervention program to determine the improvement in body composition in overweight and obese youth by incorporating the family along with the pediatric participant. Methods: We analyzed data from 884 overweight (BMI between 85% and 95th percentile) or obese (BMI N 95th percentile) youth (5 to 17 years) in a major metropolitan area in Illinois, to determine body composition improvement from an 8-week intervention between 2010 and 2017. Weight, percent body fat, fat free mass and BMI were analyzed using mixed model analysis, ANCOVA and paired t-test analysis. Results: All measures of weight, percent body fat, fat free mass and BMI improved significantly in pediatric participants. Age, county of residence and time impacted weight, body fat, fat free mass and BMI. Gender did not impact the average change in weight or BMI. Conclusions: The ProActive Kids early intervention program utilizes a targeted approach for the treatment of pediatric obesity during critical developmental ages. By educating parents along with children and adolescents about mental health coaching (lifestyle), nutrition and physical activity, we observed improvements in body composition that have potential to be sustainable. Practice Implications: The ProActive Kids strategy provides a successful model for future treatment and prevention of pediatric obesity.

This paper presents a permutation test for the incomplete pairs setting. This situation arises in both observational and experimental studies when some of the data are in the form of a paired sample and the rest of the data comprise two independent samples. The proposed method uses the data from the two types of samples to test the difference between the mean responses. Our test statistic combines the observed mean difference for the complete pairs with the difference between the two means of the independent samples. The randomizations are carried out as is typically done with standard permutation tests for paired and independent samples. We show by a simulation study that our statistic performs well in comparison to other methods.