J
John Neuhaus
Researcher at University of California, San Francisco
Publications - 304
Citations - 23514
John Neuhaus is an academic researcher from University of California, San Francisco. The author has contributed to research in topics: Covariate & Generalized linear mixed model. The author has an hindex of 73, co-authored 283 publications receiving 20432 citations. Previous affiliations of John Neuhaus include Children's Oncology Group & Leipzig University.
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Journal ArticleDOI
Sensitivity of revised diagnostic criteria for the behavioural variant of frontotemporal dementia.
Katya Rascovsky,John R. Hodges,David S. Knopman,Mario F. Mendez,Joel H. Kramer,John Neuhaus,John C. van Swieten,Harro Seelaar,Elise G.P. Dopper,Chiadi U. Onyike,Argye E. Hillis,Keith A. Josephs,Bradley F. Boeve,Andrew Kertesz,William W. Seeley,Katherine P. Rankin,Julene K. Johnson,Maria Luisa Gorno-Tempini,Howard J. Rosen,Caroline E. Prioleau-Latham,Albert Lee,Christopher M. Kipps,Christopher M. Kipps,Patricia Lillo,Olivier Piguet,Jonathan D. Rohrer,Martin N. Rossor,Jason D. Warren,Nick C. Fox,Douglas Galasko,David P. Salmon,Sandra E. Black,M.-Marsel Mesulam,Sandra Weintraub,Brad C. Dickerson,Janine Diehl-Schmid,Florence Pasquier,Vincent Deramecourt,Florence Lebert,Yolande A.L. Pijnenburg,Tiffany W. Chow,Facundo Manes,Jordan Grafman,Stefano F. Cappa,Morris Freedman,Murray Grossman,Bruce L. Miller +46 more
TL;DR: The revised criteria for behavioural variant frontotemporal dementia improve diagnostic accuracy compared with previously established criteria in a sample with known frontotmporal lobar degeneration and reflect the optimized diagnostic features, less restrictive exclusion features and a flexible structure that accommodates different initial clinical presentations.
OtherDOI
Generalized Linear Mixed Models
TL;DR: Generalized linear mixed models are a class of statistical models that handle a wide variety of distributions for the outcome, accommodate nonlinear models, and model correlated data that are capable of estimation and testing of covariate effects.
Journal ArticleDOI
A comparison of lorazepam, diazepam, and placebo for the treatment of out-of-hospital status epilepticus.
Brian K. Alldredge,Alan M. Gelb,S. Marshal Isaacs,Megan D. Corry,Faith Allen,SueKay Ulrich,Mildred D. Gottwald,Nelda O'Neil,John Neuhaus,Mark R. Segal,Daniel H. Lowenstein,Daniel H. Lowenstein +11 more
TL;DR: Benzodiazepines are safe and effective when administered by paramedics for out-of-hospital status epilepticus in adults and Lorazepam is likely to be a better therapy than diazepam.
Journal ArticleDOI
Unanticipated admission to the hospital following ambulatory surgery.
TL;DR: The results indicate that the likelihood of unanticipated admission is related more to the type of anesthesia and surgical procedure rather than to the patient's clinical characteristics.
Journal ArticleDOI
A Comparison of Cluster-Specific and Population-Averaged Approaches for Analyzing Correlated Binary Data
TL;DR: It is shown that, unlike models for correlation Gaussian outcomes, the parameters of the cluster-specific and population-averaged models for correlated binary data describe different types of effects of the covariates on the response probabilities.