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Table 2 Univariate regression analysis

From: Prediction models for early diagnosis of actinomycotic osteomyelitis of the jaw using machine learning techniques: a preliminary study

Label

Levels

Negative

Positive

OR (univariable)

Gender

Female

62 (58.5)

44 (41.5)

–

 

Male

90 (77.6)

26 (22.4)

0.41 (0.23–0.72, p = 0.003)

Age (years)

Mean (SD)

62.3 (16.1)

75.0 (12.3)

1.07 (1.04–1.10, p < 0.001)

Presumed causes

Odontogenic infection

136 (84.0)

26 (16.0)

–

 

Dental extraction

5 (15.6)

27 (84.4)

28.25 (10.74–89.57, p < 0.001)

 

Implant

3 (30.0)

7 (70.0)

12.21 (3.17–59.54, p = 0.001)

 

Unknown

8 (44.4)

10 (55.6)

6.54 (2.37–18.68, p < 0.001)

Anatomical site

Maxilla posterior

40 (69.0)

18 (31.0)

–

 

Maxilla anterior

9 (69.2)

4 (30.8)

0.99 (0.24–3.48, p = 0.985)

 

Mandible posterior

99 (70.2)

42 (29.8)

0.94 (0.49–1.86, p = 0.862)

 

Mandible anterior

4 (40.0)

6 (60.0)

3.33 (0.85–14.45, p = 0.088)

Comorbidities

Hypertension

68 (60.2)

45 (39.8)

2.22 (1.25–4.03, p = 0.007)

 

Diabetes mellitus

40 (67.8)

19 (32.2)

1.04 (0.54–1.96, p = 0.897)

 

Heart disease

35 (71.4)

14 (28.6)

0.84 (0.41–1.65, p = 0.614)

 

Renal disease

18 (72.0)

7 (28.0)

0.83 (0.31–2.01, p = 0.687)

 

Liver disease

9 (90.0)

1 (10.0)

0.23 (0.01–1.26, p = 0.168)

 

Cerebral disease

13 (92.9)

1 (7.1)

0.15 (0.01–0.80, p = 0.075)

 

Malignancy

7 (41.2)

10 (58.8)

3.45 (1.27–9.91, p = 0.016)

 

Rheumatoid arthritis

1 (20.0)

4 (80.0)

9.15 (1.32–180.87, p = 0.050)

Antiresorptive agents

 

16 (28.1)

41 (71.9)

12.02 (6.07–24.89, p < 0.001)

Antithrombotic agents

 

44 (63.8)

25 (36.2)

1.36 (0.74–2.48, p = 0.312)

  1. OR odds ratio, SD standard deviation