Skip to main content

Advertisement

Table 5 Logistic regression analysis and generalized estimating equations were used to model the real infarct diagnosis on different observed confidence scores for the corresponding modality

From: The predictive value of a targeted posterior fossa multimodal stroke protocol for the diagnosis of acute posterior ischemic stroke

  Logistic regression model GEEs method
  r2 AIC p-value OR (95 % CI) QIC p-value
NCCT
Model fit statistics 0.0856 840.6    840.6  
Observed Confidence Score    < 0.0001 0.67 (0.61–0.74)   < 0.0001
NCCT + CTA-SI
Model fit statistics 0.2578 686.7    686.7  
Observed Confidence Score    < 0.0001 0.45 (0.39–0.51)   < 0.0001
NCCT + CTA-SI + CTP
Model fit statistics 0.321 621    620.9  
Observed Confidence Score    < 0.0001 0.47 (0.41–0.52)   < 0.0001
  1. AIC Akaike information criterion, QIC quasilikelihood information criterion, NCCT non-contrast CT, CTA-SI CTA Source Images, CTP CT perfusion