Orthogeriatrics is increasingly recommended in the care of hip fracture patients, although evidence for this model is conflicting or at least limited. Furthermore, there is no conclusive evidence on which model [geriatric medicine consultant service (GCS), geriatric medical ward with orthopedic surgeon consultant service (GW), integrated care model (ICM)] is superior. The review summarizes the effect of orthogeriatric care for hip fracture patients on length of stay (LOS), time to surgery (TTS), in-hospital mortality, 1-year mortality, 30-day readmission rate, functional outcome, complication rate, and cost. Two independent reviewers retrieved randomized controlled trials, controlled observational studies, and pre/post analyses. Random-effects meta-analysis was performed. Thirty-seven studies were included, totaling 37.294 patients. Orthogeriatric care significantly reduced LOS [mean difference (MD) − 1.55 days, 95% confidence interval (CI) (− 2.53; − 0.57)], but heterogeneity warrants caution in interpreting this finding. Orthogeriatrics also resulted in a 28% lower risk of in-hospital mortality [95%CI (0.56; 0.92)], a 14% lower risk of 1-year mortality [95%CI (0.76; 0.97)], and a 19% lower risk of delirium [95%CI (0.71; 0.92)]. No significant effect was observed on TTS and 30-day readmission rate. No consistent effect was found on functional outcome. Numerically lower numbers of complications were observed in orthogeriatric care, yet some complications occurred more frequently in GW and ICM. Limited data suggest orthogeriatrics is cost-effective. There is moderate quality evidence that orthogeriatrics reduces LOS, in-hospital mortality, 1-year mortality, and delirium of hip fracture patients and may reduce complications and cost, while the effect on functional outcome is inconsistent. There is currently insufficient evidence to recommend one or the other type of orthogeriatric care model.
Goodness-of-fit tests based on the empirical Wasserstein distance are proposed for simple and composite null hypotheses involving general multivariate distributions. For group families, the procedure is to be implemented after preliminary reduction of the data via invariance. This property allows for calculation of exact critical values and p-values at finite sample sizes. Applications include testing for location-scale families and testing for families arising from affine transformations, such as elliptical distributions with given standard radial density and unspecified location vector and scatter matrix. A novel test for multivariate normality with unspecified mean vector and covariance matrix arises as a special case. For more general parametric families, we propose a parametric bootstrap procedure to calculate critical values. The lack of asymptotic distribution theory for the empirical Wasserstein distance means that the validity of the parametric bootstrap under the null hypothesis remains a conjecture. Nevertheless, we show that the test is consistent against fixed alternatives. To this end, we prove a uniform law of large numbers for the empirical distribution in Wasserstein distance, where the uniformity is over any class of underlying distributions satisfying a uniform integrability condition but no additional moment assumptions. The calculation of test statistics boils down to solving the well-studied semi-discrete optimal transport problem. Extensive numerical experiments demonstrate the practical feasibility and the excellent performance of the proposed tests for the Wasserstein distance of order p = 1 and p = 2 and for dimensions at least up to d = 5. The simulations also lend support to the conjecture of the asymptotic validity of the parametric bootstrap.
Goodness-of-fit tests based on the empirical Wasserstein distance are proposed for simple and composite null hypotheses involving general multivariate distributions. This includes the important problem of testing for multivariate normality with unspecified mean vector and covariance matrix and, more generally, testing for elliptical symmetry with given standard radial density and unspecified location and scatter parameters. The calculation of test statistics boils down to solving the well-studied semidiscrete optimal transport problem. Exact critical values can be computed for some important particular cases, such as null hypotheses of ellipticity with given standard radial density and unspecified location and scatter; else, approximate critical values are obtained via parametric bootstrap. Consistency is established, based on a result on the convergence to zero, uniformly over certain families of distributions, of the empirical Wasserstein distance-a novel result of independent interest. A simulation study establishes the practical feasibility and excellent performance of the proposed tests.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.