Table 5 Multivariable regression results and key diagnostics.(A) multivariable linear regression for abnormality count and (B) logistic regression for selected specific abnormalities.

A) Multivariable linear regression (abnormality count)

Abnormality count (continuous)

BMI (kg/m²)

0.12

0.09–0.15

< 0.001

R² = 0.43

Age (years)

0.08

0.05–0.11

< 0.001

Adjusted R² = 0.41

Work experience (years)

0.06

0.03–0.09

< 0.01

VIF max = 1.84

B) Logistic regression for specific abnormalities

Lumbar lordosis (yes/no)

BMI ≥ 30 vs. < 30

2.3

1.5–3.5

< 0.001

Nagelkerke R² = 0.28

Genu varum (yes/no)

BMI ≥ 30 vs. < 30

1.9

1.3–2.8

< 0.01

Hosmer–Lemeshow p = 0.73

Genu recurvatum (yes/no)

BMI ≥ 30 vs. < 30

1.6

1.1–2.4

< 0.05

B) Model diagnostics

Multicollinearity (VIF max)

1.84

Normality of residuals (Shapiro–Wilk)

p = 0.062

Heteroscedasticity (Breusch–Pagan)

p = 0.119

Influential observations (Cook’s D)

max = 0.0073 (threshold = 0.0096)