Chesney S Oravec1, Mustafa Motiwala1, Kevin Reed1, Tamekia L Jones2,3, Paul Klimo4,5,6. 1. College of Medicine, University of Tennessee Health Science Center, Memphis, Tennessee, USA. 2. Departments of Pediatrics and Preventive Medicine, University of Tennessee Health Science Center, Memphis, Tennessee, USA. 3. Children's Foundation Research Institute, Le Bonheur Children's Hospital, Memphis, Tennessee, USA. 4. Department of Neurosurgery, University of Tennessee Health Science Center, Memphis, Tennessee, USA, atomkpnk@yahoo.com. 5. Semmes Murphey, Memphis, Tennessee, USA, atomkpnk@yahoo.com. 6. Le Bonheur Children's Hospital, Memphis, Tennessee, USA, atomkpnk@yahoo.com.
Abstract
BACKGROUND/AIMS: We sought to describe pediatric "big data" publications since 2000, their statistical output, and clinical implications. METHODS: We searched 4 major North American neurosurgical journals for articles utilizing non-neurosurgery-specific databases for clinical pediatric neurosurgery research. Articles were analyzed for descriptive and statistical information. We analyzed effect sizes (ESs), confidence intervals (CIs), and p values for clinical relevance. A bibliometric analysis was performed using several key citation metrics. RESULTS: We identified 74 articles, which constituted 1.7% of all pediatric articles (n = 4,436) published, with an exponential increase after 2013 (53/74, 72%). The Healthcare Cost and Utilization Project (HCUP) databases were most frequently utilized (n = 33); hydrocephalus (n = 19) was the most common study topic. The statistical output (n = 49 studies with 464 ESs, 456 CIs, and 389 p values) demonstrated that the majority of the ESs (253/464, 55%) were categorized as small; half or more of the CI spread (CIS) values and p values were high (274/456, 60%) and very strong (195/389, 50%), respectively. Associations with a combination of medium-to-large ESs (i.e., magnitude of difference), medium-to-high CISs (i.e., precision), and strong-to-very strong p values comprised only 20% (75/381) of the reported ESs. The total number of citations for the 74 articles was 1,115 (range per article, 0-129), with the median number of citations per article being 8.5. Four studies had > 50 citations, and 2 of them had > 100 citations. The calculated h-index was 16, h-core citations were 718, the e-index was 21.5, and the Google i10-index was 34. CONCLUSIONS: There has been a dramatic increase in the use of "big data" in the pediatric neurosurgical literature. Reported associations that may, as a group, be of greatest interest to practitioners represented only 20% of the total output from these publications. Citations were weighted towards a few highly cited publications.
BACKGROUND/AIMS: We sought to describe pediatric "big data" publications since 2000, their statistical output, and clinical implications. METHODS: We searched 4 major North American neurosurgical journals for articles utilizing non-neurosurgery-specific databases for clinical pediatric neurosurgery research. Articles were analyzed for descriptive and statistical information. We analyzed effect sizes (ESs), confidence intervals (CIs), and p values for clinical relevance. A bibliometric analysis was performed using several key citation metrics. RESULTS: We identified 74 articles, which constituted 1.7% of all pediatric articles (n = 4,436) published, with an exponential increase after 2013 (53/74, 72%). The Healthcare Cost and Utilization Project (HCUP) databases were most frequently utilized (n = 33); hydrocephalus (n = 19) was the most common study topic. The statistical output (n = 49 studies with 464 ESs, 456 CIs, and 389 p values) demonstrated that the majority of the ESs (253/464, 55%) were categorized as small; half or more of the CI spread (CIS) values and p values were high (274/456, 60%) and very strong (195/389, 50%), respectively. Associations with a combination of medium-to-large ESs (i.e., magnitude of difference), medium-to-high CISs (i.e., precision), and strong-to-very strong p values comprised only 20% (75/381) of the reported ESs. The total number of citations for the 74 articles was 1,115 (range per article, 0-129), with the median number of citations per article being 8.5. Four studies had > 50 citations, and 2 of them had > 100 citations. The calculated h-index was 16, h-core citations were 718, the e-index was 21.5, and the Google i10-index was 34. CONCLUSIONS: There has been a dramatic increase in the use of "big data" in the pediatric neurosurgical literature. Reported associations that may, as a group, be of greatest interest to practitioners represented only 20% of the total output from these publications. Citations were weighted towards a few highly cited publications.