Retrospective descriptive cohort study.To describe the distribution and rate of injuries in elite adolescent ballet dancers, and to examine the utility of screening data to distinguish between injured and noninjured dancers.Adolescent dancers account for most ballet injuries. Limited information exists, however, regarding the distribution of, rate of, and risk factors for, adolescent dance injuries.Two hundred four dancers (age, 9-20 years) were screened over 5 years. Screening data were collected at the beginning and injury data were collected at the end of each training year. Descriptive statistics were used to characterize distribution and rate of injuries. Inference statistics were used to examine differences between injured and noninjured dancers.Fifty-three percent of injuries occurred in the foot/ankle, 21.6% in the hip, 16.1% in the knee, and 9.4% in the back. Thirty-two to fiftyone percent of the dancers were injured each year, and, over the 5 years, there were 1.09 injuries per 1000 athletic exposures, and 0.77 injuries per 1000 hours of dance. Significant differences between injured and noninjured dancers were limited to current disability scores (P = .007), history of low back pain (P = .017), right foot pronation (P = .005), insufficient right-ankle plantar flexion (P = .037), and lower extremity strength (P = .045).Distribution of injuries was similar to that of other studies. Injury rates were lower than most reported rates, except when expressed per 1000 hours of dance. Few differences were found between injured and noninjured dancers. These findings should be considered when designing and implementing screening programs.Prognosis, level 2b.
This paper discusses the synthesis of partial effect sizes derived from multivariate settings. The general statistical properties of the d-effect size are derived, extending Hedges's statement of zero-order properties. These general properties have direct relevance in the synthesis of a set of independent effect sizes arising from empirical studies predicated on a common theoretical model. We discuss possible solutions to the problem of comparing effect sizes arising from models employing differing sets of covariates. We apply the general statistical properties in the synthesis of gender performance differences in first-level economics courses at three New Zealand universities. The model of academic performance is based on Spearman's conception of general academic ability and specific ability in economics.
Recently Lapointe et. al. [3] have expressed Jack Polynomials as determinants in monomial symmetric functions $m_\lambda$. We express these polynomials as determinants in elementary symmetric functions $e_\lambda$, showing a fundamental symmetry between these two expansions. Moreover, both expansions are obtained indifferently by applying the Calogero-Sutherland operator in physics or quasi Laplace Beltrami operators arising from differential geometry and statistics. Examples are given, and comments on the sparseness of the determinants so obtained conclude the paper.
We obtain simple and generally applicable conditions for the existence of mixed moments E([X′ AX]″/[X′BX]U) of the ratio of quadratic forms T = X′ AX/X′BX where A and B are n × n symmetric matrices and X is a random n-vector. Our principal theorem is easily stated when X has an elliptically symmetric distribution, which class includes the multivariate normal and t distributions, whether degenerate or not. The result applies to the ratio of multivariate quadratic polynomials and can be expected to remain valid in most situations in which X is subject to linear constraints.If u ≤ v, the precise distribution of X, and in particular the existence of moments of X, is virtually irrelevant to the existence of the mixed moments of T; if u > v, a prerequisite for existence of the (u, v)th mixed moment is the existence of the 2(u − v)th moment of X When Xis not degenerate, the principal further requirement for the existence of the mixed moment is that B has rank exceeding 2v.
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