Table 3:

Progressive increase of multiple entropy r2 value and decrease of AIC with the incremental protocola

Logistic Regression ModelGEEs Method
r2AICP ValueOdds Ratio (95% CI)AICP Value
    Model fit statistics0.015451.1451.3
    Observed diagnosis (yes vs no).281.68 (0.65–4.41).29
    Observed confidence score.901.03 (0.67–1.58).90
    Model fit statistics0.170409.2408.7
    Observed diagnosis (yes vs no).590.78 (0.31–1.94).57
    Observed confidence score<.001b0.46 (0.31–0.68)<.001b
    Model fit statistics0.329357.3357.1
    Observed diagnosis (yes vs no).04b0.33 (0.11–0.95).05
    Observed confidence score<.001b0.30 (0.20–0.44)<.001b
  • Note:—GEEs indicates generalized estimating equations.

  • a With logistic regression analysis and the GEEs method, the actual stroke diagnosis was modelled on different observed diagnoses (NCCT alone, NCCT + CTA-SI, NCCT + CTA-SI + CTP) when adjusting for the corresponding confidence score. OR < 1 indicates that patients with a positive diagnosis on MRI are more likely to have a lower level of confidence (1 = definitely present, 2 = probably present, 3 = possibly present, 4 = possibly absent, 5 = probably absent, 6 = definitely absent).

  • b Statistically significant.