To study the prevalence of thyroid disorders in chronic renal disease patients

2020 ◽  
Vol 7 (1) ◽  
pp. 20
Author(s):  
MahmoodDhahir Al-Mendalawi
2018 ◽  
Vol 5 (4) ◽  
pp. 126
Author(s):  
Prashant Prakash ◽  
UpendraNath Gupta ◽  
Apoorva Jain ◽  
Prabhat Agrawal ◽  
Ravi Kumar ◽  
...  

1970 ◽  
Vol 126 (5) ◽  
pp. 774-780 ◽  
Author(s):  
A. J. Erslev

1971 ◽  
Author(s):  
Virgil Smirnow ◽  
Robert J. Shaloub ◽  
Jonathan W. Cummings ◽  
Vincent Glaudin ◽  
Roy Brener ◽  
...  

2001 ◽  
Vol 11 (4) ◽  
pp. 183-193 ◽  
Author(s):  
Tedine Ranich ◽  
Sam J. Bhathena ◽  
Manuel T. Velasquez

2001 ◽  
Vol 21 (1) ◽  
pp. 3-12 ◽  
Author(s):  
Amin Al-Ahmad ◽  
Mark J. Sarnak ◽  
Deeb N. Salem ◽  
Marvin A. Konstam

2001 ◽  
Vol 21 (1) ◽  
pp. 66-78 ◽  
Author(s):  
Marilyn M. Barbour ◽  
David S. McKindley

1980 ◽  
Vol 19 (04) ◽  
pp. 205-209
Author(s):  
L. A. Abbott ◽  
J. B. Mitton

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.


1951 ◽  
Vol 8 (2) ◽  
pp. 165-174
Author(s):  
SVEN JOHNSSON ◽  
ROLF LUFT ◽  
BJÖRN SJÖGREN ◽  
JAN WALDENSTRÖM

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