Subacute casemix classification for stroke rehabilitation in Australia. How well does AN-SNAP v2 explain variance in outcomes?

2011 ◽  
Vol 35 (1) ◽  
pp. 1 ◽  
Author(s):  
Friedbert Kohler ◽  
Roger Renton ◽  
Hugh G. Dickson ◽  
John Estell ◽  
Carol E. Connolly
Keyword(s):  
Length Of Stay ◽  
Principal Components Analysis ◽  
Principal Components ◽  
Explanatory Variable ◽  
Inpatient Rehabilitation ◽  
Medical Rehabilitation ◽  
Functional Improvement ◽  
Discharge Destination ◽  
Components Analysis ◽  
Classification Tool

Objective. We sought the best predictors for length of stay, discharge destination and functional improvement for inpatients undergoing rehabilitation following a stroke and compared these predictors against AN-SNAP v2. Method. The Oxfordshire classification subgroup, sociodemographic data and functional data were collected for patients admitted between 1997 and 2007, with a diagnosis of recent stroke. The data were factor analysed using Principal Components Analysis for categorical data (CATPCA). Categorical regression analyses was performed to determine the best predictors of length of stay, discharge destination, and functional improvement. Results. A total of 1154 patients were included in the study. Principal components analysis indicated that the data were effectively unidimensional, with length of stay being the most important component. Regression analysis demonstrated that the best predictor was the admission motor FIM score, explaining 38.9% of variance for length of stay, 37.4%.of variance for functional improvement and 16% of variance for discharge destination. Conclusion. The best explanatory variable in our inpatient rehabilitation service is the admission motor FIM. AN- SNAP v2 classification is a less effective explanatory variable. This needs to be taken into account when using AN-SNAP v2 classification for clinical or funding purposes. What is known about the topic? AN-SNAP v2, a major classification tool for inpatient rehabilitation units has been described and used in a small number of published studies. The ability to predict variance by AN-SNAP v2 has not been previously described. What does this paper add? This paper indicates that AN-SNAP v2 is not a good predictor of outcomes in patients in medical rehabilitation units, challenging its utility as a classification tool. What are the implications for practitioners? Practitioners will have a broader understanding of the strengths and weaknesses of the AN-SNAP v2 classification.

Diagnoses Generated by Numerical Taxonomic Methods Applied to Standard Blood Variables

1980 ◽  
Vol 19 (04) ◽  
pp. 205-209
Author(s):  
L. A. Abbott ◽  
J. B. Mitton
Keyword(s):  
Liver Cirrhosis ◽  
Renal Disease ◽  
Principal Components Analysis ◽  
Discriminant Function ◽  
Principal Components ◽  
Discriminant Function Analysis ◽  
Chronic Renal Disease ◽  
Function Analysis ◽  
Infectious Hepatitis ◽  
Components Analysis

Data taken from the blood of 262 patients diagnosed for malabsorption, elective cholecystectomy, acute cholecystitis, infectious hepatitis, liver cirrhosis, or chronic renal disease were analyzed with three numerical taxonomy (NT) methods : cluster analysis, principal components analysis, and discriminant function analysis. Principal components analysis revealed discrete clusters of patients suffering from chronic renal disease, liver cirrhosis, and infectious hepatitis, which could be displayed by NT clustering as well as by plotting, but other disease groups were poorly defined. Sharper resolution of the same disease groups was attained by discriminant function analysis.

Assamese vowels and vowel harmony

2020 ◽  
Vol 6 (2) ◽  
pp. 151-183
Author(s):  
Diana B. Archangeli ◽  
Jonathan Yip
Keyword(s):  
Physical Properties ◽  
Principal Components Analysis ◽  
Principal Components ◽  
Vowel Harmony ◽  
Language Community ◽  
Acoustic Data ◽  
Apparent Loss ◽  
Root Position ◽  
Components Analysis ◽  
Open Question

AbstractBased on impressionistic and acoustic data, Assamese is described as having a phonological tongue root harmony system, with blocking by certain phonological configurations and over-application in certain morphological contexts. This study explores physical properties of the patterns using ultrasonic imaging to determine whether the impressionistic descriptions match what speakers actually do. Principal components analysis (PCA) determines that most participants produce a contrast in tongue root position in the appropriate contexts, though there is less of an impact on tongue root with greater distance from the triggering vowel. Analysis uses the root mean squared distance (RMSD) calculation to determine whether both blocking and over-application take effect. The blocking results conform to the impressionistic descriptions. With over-application, [e] and [o] are expected; while some speakers clearly produce these vowels, others articulate a vowel that is indeterminant between the expected [e]/[o] and an unexpected [ɛ]/[ɔ]. No speaker consistently showed the expected tongue root position in all contexts, and some speakers appeared to have lost the contrast entirely, yet all are considered to be speakers of the same dialect of Assamese. Whether this (apparent) loss is a consequence of crude research methodologies or accurately reflects what is happening within the language community remains an open question.

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