Table 4.

Summary using algorithm cut-off scores of ADOS-2 manual

Module 1 Algorithm 1	No ASD (N=40)	ASD (N=324)	All (N=364)	Correctly predicting BEC diagnosis
non-spectrum (0–10)	25 (62.5%)	3 (0.9%)	28 (7.7%)	89%
autism spectrum (11–15)	8 (20%)	30 (9.3%)	38 (10.4%)	79%
autism (16–28)	7 (17.5%)	291 (89.8%)	298 (81.9%)	98%
	TN 62%, FP 38%	TP 99%, FN 1%
Module 1 Algorithm 2	No ASD (N=129)	ASD (N=405)	All (N=534)	Correctly predicting BEC diagnosis
non-spectrum (0–7)	82 (63.6%)	4 (1%)	86 (16.1%)	95%
autism spectrum (8–11)	28 (21.7%)	36 (8.9%)	64 (12%)	56%
autism (12–28)	19 (14.7%)	365 (90.1%)	384 (71.9%)	95%
	TN 64%, FP 36%	TP 99%, FN 1%
Module 2 Algorithm 1	No ASD (N=200)	ASD (N=213)	All (N=413)	Correctly predicting BEC diagnosis
non-spectrum (0–6)	138 (69%)	6 (2.8%)	144 (34.9%)	96%
autism spectrum (7–9)	34 (17%)	29 (13.6%)	63 (15.3%)	46%
autism (10–28)	28 (14%)	178 (83.6%)	206 (49.9%)	86%
	TN 69%, FP 31%	TP 97%, FN 3%
Module 2 Algorithm 2	No ASD (N=98)	ASD (N=159)	All (N=257)	Correctly predicting BEC diagnosis
non-spectrum (0–7)	64 (65.3%)	6 (3.8%)	70 (27.2%)	91%
autism spectrum (8)	11 (11.2%)	3 (1.9%)	14 (5.4%)	21%
autism (9–28)	23 (23.5%)	150 (94.3%)	173 (67.3%)	86%
	TN 65%, FP 35%	TP 96%, FN 4%
Module 3	No ASD (N=812)	ASD (N=764)	All (N=1576)	Correctly predicting BEC diagnosis
non-spectrum (0–6)	593 (73%)	41 (5.4%)	634 (40.2%)	94%
autism spectrum (7–8)	79 (9.7%)	100 (13.1%)	179 (11.4%)	56%
autism (9–28)	140 (17.2%)	623 (81.5%)	763 (48.4%)	82%
	TN 73%, FP 27%	TP 95%, FN 5%
Total sample	No ASD (N=1279)	ASD (N=1865)	All (N=3144)	Correctly predicting BEC diagnosis
non-spectrum	902 (70.5%)	60 (3.3%)	962 (30.7%)	94%
autism spectrum	160 (12.5%)	198 (10.6%)	358 (11.3%)	56%
autism	217 (17%)	1607 (86.2%)	1824 (58%)	88%
	TN 71%, FP 29%	TP 97%, FN 3%

TN: true negative, FP: false positive, TP: true positive, FN: false negative