Table 2 Model fit evaluation of comprehensive (ALCS) and empirical (ALES) algorithms^a

a. Likelihood ratio tests for original, corrected and IL6 biomarker inclusion algorithms
ALCS-original	χ² (9)= 10.371, p= 0.321		ALES-original		χ² (9)= 16.309, p= 0.061
ALCS-corrected	χ² (9)= 17.098, p= 0.047		ALES-corrected		χ² (9)= 19.012, p= 0.025*
ALCS-corrected + IL-6	χ² (9)= 16.952, p= 0.049		ALES-corrected + IL-6		χ² (9)= 20.844, p= 0.013*
b. Comparison between algorithms showing best model fit
	Model fitting	Pseudo R²	Δ R²	BIC	Δ BIC	Log-likelihood (LL)	Δ LL
ALES-corrected	χ² (9)= 19.012, p= 0.025	0.048	REF	408.54	REF	−173.599	REF
ALES-corrected +IL-6	χ² (9)= 20.844, p= 0.013	0.054	0.006	396.19	−12.356^b	−167.421	6.178
Rate of correct classification			Very-low	Low	Medium	High	Overall
ALES-corrected		observed	6	39	53	68	166
		predicted	0	1	20	51	72
		% correct	0	2.6	37.7	75	43.4
ALES-corrected +IL-6		observed	6	36	79	45	166
		predicted	0	5	70	5	80
		% correct	0	13.9	88.6	11.1	48.2
p̂ Comparisons		pooled p̂	0	0.08	0.682	0.496	0.458
		SEDp	0	0.297	0.118	0.234	0.081
		z	0	0.381	4.308	2.727	0.595
		p (a = 0.05)	0.5	0.352	<0.001	0.003	0.276
Inter-rater reliability
p Agreement	p Error	Cohen's κ	SEκ	95% CI		Z transformed	P
0.843	0.023	0.798	0.046	0.707	0.889	17.199	<0.001

BIC Bayesian Information Criterion, BIC Δ Difference in BIC compared with the classic empirical AL scoring algorithm.
*Satisfactory model fit (α = 0.05).
^aMultinomial logistic regression (MLR): AL category = Sex + Age + Years of education + ε.
^bSubstantial decrease in BIC.

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