Objectives: The aim was to develop and validate the Communities Geriatric Mild Cognitive Impairment Risk Calculator (CGMCI-Risk), aiding community healthcare workers in the early identification of individuals at high risk of mild cognitive impairment (MCI). Methods: Based on nationally representative community survey data, backward stepwise regression was employed to screen the variables, and logistic regression was utilized to construct the CGMCI-Risk. Internal validation was conducted using bootstrap resampling, while external validation was performed using temporal validation. The area under the receiver operating characteristic curve (AUROC), calibration curve, and decision curve analysis (DCA) were employed to evaluate the CGMCI-Risk in terms of discrimination, calibration, and net benefit, respectively. Results: The CGMCI-Risk model included variables such as age, educational level, sex, exercise, garden work, TV watching or radio listening, Instrumental Activity of Daily Living (IADL), hearing, and masticatory function. The AUROC was 0.781 (95% CI = 0.766 to 0.796). The calibration curve showed strong agreement, and the DCA suggested substantial clinical utility. In external validation, the CGMCI-Risk model maintained a similar performance with an AUROC of 0.782 (95% CI = 0.763 to 0.801). Conclusions: CGMCI-Risk is an effective tool for assessing cognitive function risk within the community. It uses readily predictor variables, allowing community healthcare workers to identify the risk of MCI in older adults over a three-year span.

Aim: To analyse the application status of nursing assessment terminology for neurological conditions and determine whether the International Classification of Functioning, Disability, and Health (ICF) covers nursing assessment.Design: A multi‐centre cross‐sectional study.Methods: Four researchers extracted all nursing problems from the patients of three different hospitals and formed a pool of nursing terminology from the electronic nursing records, self‐reports, family reports, medical examinations, and clinical records for all patients. The ICF Linking Rules were then used to map the nursing assessment terminology of neurological conditions with the ICF.Results: Though 37.5% of nursing assessment terms were closely related to neurological diseases, this does not appear in the existing electronic nursing assessment records. The unrecorded rate of 9 (16.1%) terms ranged from 40%–50%, while the unrecorded rate of 8 (14.3%) terms was more than 80%. Overall, 96.4% of nursing assessment terms could be described by the corresponding categories of the ICF, with 37 (66.1%) of the “same” concepts, 9 (16.1%) “similar” concepts, 6 (10.7%) “narrower” concepts (the nursing assessment terms were more specific than the ICF categories), and 2 (3.6%) “broader” concepts (the nursing assessment were less specific than the ICF categories).

Objectives: To develop and validate the Communities Geriatric Mild Cognitive Impairment Risk Calculator (CGMCI-Risk), aimed at aiding community healthcare workers in the early identification of individuals at high risk of Mild Cognitive Impairment (MCI); Methods: Based on nationally representative community survey data, backward stepwise regression was employed to screen the variables, and logistic regression was utilized to construct the CGMCI-Risk. Internal validation was conducted using bootstrap resampling, while external validation was performed using temporal validation. The area under the receiver operating characteristic curve (AUROC), calibration curve, and decision curve analysis (DCA) were employed to evaluate the CGMCI-Risk in terms of discrimination, calibration, and net benefit, respectively; Results: The CGMCI-Risk model included variables such as age, educational level, sex, exercise, garden work, TV watching or radio listening, Instrumental Activity of Daily Living (IADL), hearing, and masticatory function. The AUROC was 0.781 (95% CI = 0.766 to 0.796). Calibration curve showed strong agreement, and the DCA suggested substantial clinical utility. In external validation, the CGMCI-Risk model maintained similar performance with an AUROC of 0.782 (95% CI = 0.763 to 0.801); Conclusions: CGMCI-Risk is an effective tool for assessing cognitive function risk within the community. It uses readily predictor variables, allowing community healthcare workers to identify the risk of MCI in older adults over a three-year span.