External validation of outcome prediction model for ureteral/renal calculi

Journal of Urology

Volumn 175, Issue 2, 2006, Pages 575-579

  Parekattil, Sijo J ^a   Kumar, Udaya ^a   Hegarty, Nicholas J ^a   Williams, Clay ^a   Allen, Tara ^a   Teloken, Patrick ^a   Leitão, Victor A ^a   Netto, Nelson R ^a   Haber, Georges Pascal ^a   Ballereau, Charles ^a   Villers, Arnauld ^a   Streem, Stevan B ^a   White, Mark D ^a   Moran, Michael E ^a  

^a NONE

Author keywords

Kidney calculi; Neural networks (computer); Ureteral calculi

Indexed keywords

ADOLESCENT; ADULT; AGED; ARTICLE; ARTIFICIAL NEURAL NETWORK; BIOLOGICAL MODEL; COMPUTER PROGRAM; DISEASE COURSE; HUMAN; MAJOR CLINICAL STUDY; MATHEMATICAL ANALYSIS; NEPHROLITHIASIS; PATHOPHYSIOLOGY; PREDICTION; PRIORITY JOURNAL; RECEIVER OPERATING CHARACTERISTIC; SCHOOL CHILD; SEX DIFFERENCE; URETER STONE; VALIDATION PROCESS;

ADOLESCENT; ADULT; AGED; AGED, 80 AND OVER; CHILD; FEMALE; HUMANS; KIDNEY CALCULI; MALE; MIDDLE AGED; NEURAL NETWORKS (COMPUTER); REMISSION, SPONTANEOUS; REPRODUCIBILITY OF RESULTS; URETERAL CALCULI;

EID: 30344444813     PISSN: 00225347     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0022-5347(05)00244-2     Document Type: Article

References (8)

1. O.F. Miller, C.J. Kane Time to stone passage for observed ureteral calculi a guide for patient education J Urol 162 1999 688
2. D.M. Coll, M.J. Varanelli, R.C. Smith Relationship of spontaneous passage of ureteral calculi to stone size and location as revealed by unenhanced helical CT AJR Am J Roentgenol 178 2002 101
3. N. Takahashi, A. Kawashima, R.D. Ernst, I.C. Boridy, S.M. Goldman, G.S. Benson Ureterolithiasis can clinical outcome be predicted with unenhanced helical CT? Radiology 208 1998 97
4. S.J. Parekattil, M.D. White, M.E. Moran, B.A. Kogan A computer model to predict the outcome and duration of ureteral or renal calculous passage J Urol 171 2004 1436
5. D.E. Matthews, V.T. Farewell 3rd ed. Using and Understanding Medical Statistics 1996 S. Karger Publishers New York 136 chapt. 11
6. J.F. Jekel, J.G. Elmore, D.L. Katz Epidemiology, Biostatistics, and Preventive Medicine 1996 W. B. Saunders Co Philadelphia 172 chapt. 13
7. J.E. Dayhoff, J.M. DeLeo Artificial neural networks opening the black box Cancer 91 2001 1615
8. G. Schwarzer, M. Schumacher Artificial neural networks for diagnosis and prognosis in prostate cancer Semin Urol Oncol 20 2002 89