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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
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Neurovascular Imaging

ISSN: 2055-5792