Machine learning-based prediction of the necessity for the surgical treatment of distal radius fractures

Table 4 Clinical characteristics of the studied patients

Characteristics	Surgery not required	Surgery required	Total	P value
Participants	480	659	1139
Mean age (yr)	54.88 ± 16.51 (19–93)	63.09 ± 15.93 (19–96)	59.63 ± 16.67	< 0.001^*
Gender (Men / Women)	190 (39.6%) / 290 (60.4%)	158 (24.0%) / 501 (76.0%)	348 / 791	< 0.001^*
Affected sides (Right / Left)	329 (50.0%) / 329 (50.0%)	229 (34.7%) / 250 (65.3%)	558 / 579	0.465
Body mass index (kg/m²)	24.62 ± 3.74 (16.9–46.6)	23.93 ± 3.52 (14.9–35.7)	24.22 ± 3.63	0.002^*
Presence of subsequent or concomitant fracture (No / Yes)	374 (77.9%) / 106 (22.1%)	532 (80.7%) / 127 (19.3%)	906 / 233	0.245

Descriptive values are shown as mean ± standard deviation (range) or number of cases (proportion (%)); Statistical differences between the two groups were analyzed using the independent t-test for continuous variables and the Chi-square test for categorical variables
^*Adjusted P < 0.01 by chi-square test and independent t-test

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