Efficacy of a machine learning model integrating clinical-psychosocial factors in predicting posttraumatic bone nonunion and bone defects

doi:10.5498/wjp.119455

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World Journal of Psychiatry

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2220-3206

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Copyright: ©Author(s) 2026. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial (CC BY-NC 4.0) license. No commercial re-use. See permissions. Published by Baishideng Publishing Group Inc.

World J Psychiatry. Sep 19, 2026; 16(9): 119455
Published online Sep 19, 2026. doi: 10.5498/wjp.119455

Table 3 Baseline data comparison (development vs external validation), n (%)

	Development cohort (n = 145)	External validation cohort (n = 53)	t/χ²	P value
Nonunion	17 (11.72)	10 (18.87)	1.682	0.195
Age (years)	47.69 ± 14.86	49.28 ± 14.06	0.677	0.499
Gender			0.013	0.909
Male	100 (68.97)	37 (69.81)
Female	45 (31.03)	16 (30.19)
Body mass index (kg/m²)	23.87 ± 3.00	24.97 ± 3.13	2.248	0.026
Bone defect length (mm)	40.01 ± 13.00	41.92 ± 13.11	0.917	0.360
Distraction start time (days)	10.48 ± 3.88	9.51 ± 3.21	1.632	0.104
Distraction period (days)	43.07 ± 9.82	43.62 ± 8.86	0.360	0.719
Bone mineralization time (days)	201.26 ± 92.78	208.42 ± 94.27	0.479	0.633
Open fracture	38 (26.21)	15 (28.30)	0.087	0.768
Comorbidity
Hypertension	63 (43.45)	23 (43.40)	0.007	0.995
Diabetes	28 (19.31)	12 (22.64)	0.267	0.605
Atherosclerosis	25 (17.24)	10 (18.87)	0.071	0.791
Depression (PHQ-9)	11.93 ± 3.70	14.96 ± 3.30	5.249	0.000
Anxiety (GAD-7)	11.06 ± 3.95	13.13 ± 2.81	4.313	0.000
Social support (MSPSS)	57.62 ± 10.95	60.28 ± 11.21	1.505	0.134
Current smoker	44 (30.34)	16 (30.19)	0.080	0.983
Low-income earner	59 (40.69)	23 (43.40)	0.117	0.732

PHQ-9: Patient Health Questionnaire-9; GAD-7: Generalized Anxiety Disorder-7; MSPSS: Multidimensional Scale of Perceived Social Support.

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Citation: Luan FF, Chen JY, Zheng YZ, Hu MH, Huang F, Zheng CX. Efficacy of a machine learning model integrating clinical-psychosocial factors in predicting posttraumatic bone nonunion and bone defects. World J Psychiatry 2026; 16(9): 119455
URL: https://www.wjgnet.com/2220-3206/full/v16/i9/119455.htm
DOI: https://dx.doi.org/10.5498/wjp.119455

Luan FF, Chen JY, Zheng YZ, Hu MH, Huang F, Zheng CX. Efficacy of a machine learning model integrating clinical-psychosocial factors in predicting posttraumatic bone nonunion and bone defects. World J Psychiatry 2026; 16(9): 119455 [DOI: 10.5498/wjp.119455]

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