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Table 2 Models' Performances

From: The counterintuitive effect of multiple injuries in severity scoring: a simple variable improves the predictive ability of NISS

Model C statistics (95% CI) P of C statistics comparison* Hosmer-Lemeshow
C statistics
Hosmer-Lemeshow
H statistics
Akaike's information criterion
Simple      
MaxAIS 0.729
(0.699-0.758)
/ 11.52 p = 0.24 228.68 p < 0.01 1712
NISS 0.755
(0.726-0.784)
0.02 14.69 p = 0.14 7.12
p = 0.52
1635
NISS + num_inj 0.775
(0.745-0.804)
0.03 9.03
p = 0.52
10.32 p = 0.24 1602
Augmented     
MaxAIS 0.841
(0.820-0.862)
/ 11.96 p = 0.28 18.66 p = 0.04 1542
NISS 0.865
(0.844-0.886)
<0.01 7.47 p = 0.68 17.51 p = 0.06 1352
NISS + num_inj 0.874
(0.855-0.894)
0.01 7.21 p = 0.72 10.27 p = 0.41 1331
Complete     
MaxAIS 0.890
(0.872-0.909)
/ 10.69 p = 0.38 12.71 p = 0.24 1234
NISS 0.898
(0.880-0.916)
0.06 5.50 p = 0.85 15.87 p = 0.10 1174
NISS + num_inj 0.901
(0.884-0.919)
0.09 4.00 p = 0.94 9.05
p = 0.52
1167
Complete‡, NISS>15      
MaxAIS 0.888
(0.868-0.907)
/ 7.22 p = 0.70 20.79 p = 0.02 1165
NISS 0.897
(0.879-0.916)
0.03 6.92 p = 0.73 19.14 p = 0.03 1105
NISS + num_inj 0.901
(0.883-0.919)
0.05 5.76 p = 0.83 13.68 p = 0.18 1098
  1. * comparison with the preceding model in the table
  2. num_inj = an indicator variable expressing the number of injuries (1,2,3+)
  3. †augmented with age, gender and mechanism of injury
  4. ‡completed with the above variables plus systolic blood pressure and motor component of Glasgow Coma Scale