Table 4 Binary logistic regression analysis of neurocognitive functions among ado-FEP and adu-FEP, ado-HC and adu-HC groups

Ado-FEP vs. Adu-FEP (Classification accuracy: 63.8%) (Hosmer–Lemeshaw test: 11.163(0.193))

 BACS symbol coding

-0.022

0.009

0.978

0.962

0.995

6.432

0.011

 NAB mazes

-0.059

0.014

0.943

0.918

0.969

18.319

< 0.001

 BVMT-R

-0.040

0.012

0.960

0.938

0.983

11.385

0.001

 Category fluency

0.037

0.016

1.037

1.006

1.070

5.530

0.019

 CPT-IP

0.577

0.122

1.780

1.403

2.259

22.523

< 0.001

Ado-HC vs. Adu-HC (Classification accuracy: 65.7%) (Hosmer–Lemeshaw test: 6.752(0.564))

 BACS symbol coding

-0.023

0.009

0.977

0.960

0.994

6.844

0.009

 HVLT-R

-0.062

0.021

0.940

0.902

0.980

8.522

0.004

 WMS-3 spatial span

-0.081

0.029

0.923

0.871

0.977

7.521

0.006

 NAB mazes

-0.062

0.018

0.940

0.907

0.975

11.340

0.001

 Category fluency

0.059

0.015

1.060

1.029

1.093

14.363

< 0.001

 CPT-IP

0.855

0.138

2.352

1.795

3.082

38.478

< 0.001

Ado-FEP vs. Ado-HC (Classification accuracy: 81.8%) (Hosmer–Lemeshaw test: 9.130(0.331))

 BACS symbol coding

-0.107

0.014

0.898

0.875

0.923

61.738

< 0.001

 HVLT-R

-0.072

0.024

0.930

0.887

0.976

8.731

0.003

 NAB mazes

-0.047

0.021

0.954

0.916

0.994

5.124

0.024

 Category fluency

-0.055

0.022

0.947

0.907

0.988

6.317

0.012

 CPT-IP

-0.614

0.163

0.541

0.393

0.746

14.097

0.001

Adu-FEP vs. Adu-HC (Classification accuracy: 84.6%) (Hosmer–Lemeshaw test: 21.609(0.006))

 Trail making A

0.033

0.010

1.034

1.014

1.054

11.160

0.001

 BACS symbol coding

-0.083

0.012

0.920

0.898

0.943

44.766

< 0.001

 HVLT-R

-0.059

0.023

0.943

0.901

0.987

6.470

0.011

 NAB mazes

-0.064

0.020

0.938

0.902

0.975

10.658

0.001

 CPT-IP

-1.066

0.171

0.344

0.246

0.482

38.741

< 0.001