Copyright
©The Author(s) 2017.
World J Methodol. Mar 26, 2017; 7(1): 16-24
Published online Mar 26, 2017. doi: 10.5662/wjm.v7.i1.16
Published online Mar 26, 2017. doi: 10.5662/wjm.v7.i1.16
Table 4 Predictors of desirability of score automation based on number of each variable type in each score
Automation: Very important/nice to have | OR (95%CI) |
Critical care | |
n of variables | 0.68 (0.23, 1.59) |
Clinical history | 1.36 (0.36, 4.93) |
Vital sign | 1.40 (0.53, 4.6) |
Medication | 4.89 (0.10, 237.52) |
Clinical judgment | 2.33 (0.76, 9.80) |
Examination | 0.99 (0.36, 3.14) |
Laboratory value | 1.48 (0.61, 4.41) |
Charted variable (non-vital) | 2.26 (0.70, 8.93) |
Demographic value | 0.20 (0.03, 1.00) |
Another score | 2.07 (0.39, 12.13) |
Internal medicine | |
n of variables | 0.64 (0.39, 1.04) |
Clinical history | 2.34a (1.26, 4.67) |
Vital sign | 1.88a (1.03, 3.68) |
Medication | 2.89 (0.37, 63.17) |
Clinical judgment | 1.41 (0.75, 2.74) |
Examination | 1.56 (0.88, 2.87) |
Laboratory value | 1.51 (0.90, 2.62) |
Charted variable (non-vital) | 2.54 (0.85, 8.70) |
Demographic value | 0.90 (0.41, 1.97) |
Another score | 0.89 (0.30, 2.17) |
- Citation: Aakre CA, Dziadzko MA, Herasevich V. Towards automated calculation of evidence-based clinical scores. World J Methodol 2017; 7(1): 16-24
- URL: https://www.wjgnet.com/2222-0682/full/v7/i1/16.htm
- DOI: https://dx.doi.org/10.5662/wjm.v7.i1.16