Application of Screening Scale for Mild Cognitive Impairment in screening mild cognitive impairment of the elderly in rural communities in Hangzhou, Zhejiang
Bibliographic record
Abstract
Objective To investigate the prevalence of mild cognitive impairment (MCI) among the elderly in rural communities in Hangzhou, Zhejiang, and to explore the screening accuracy of Screening Scale for Mild Cognitive Impairment (sMCI) in the elderly with low education. Methods From April 2010 to September 2010, 360 elderly people in Sijiqing street, Jianggan district (now Shangcheng district), Hangzhou, Zhejiang were recruited. Dementia and MCI were judged by Mini⁃Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA), sMCI and Clinical Dementia Rating Scale (CDR). Draw receiver operating characteristic (ROC) curve and calculate the area under the curve (AUC), and compare the accuracy of sMCI, MoCA and CDR scores in screening MCI. Results Finally, 171 cases completed all investigations. 1) 55 cases (32.16%) were diagnosed as MCI, including 25 cases (14.62%) of amnestic MCI (aMCI) and 30 cases of non⁃aMCI, 11 cases (6.43%) of dementia, 10 cases (5.85%) of depression, 4 cases (2.34%) of anxiety disorder, one case (0.58%) of bipolar disorder, one case (0.58%) of schizophrenia and one case (0.58%) of mental retardation. Among 154 patients with cognitive impairment, 25 cases (16.23%) were screened for dementia by MMSE, 8 cases (5.19%) were screened for dementia by CDR, and 11 cases (7.14%) were clinically confirmed; 138 cases (89.61%) of MCI were screened by MoCA, 117 cases (75.97%) by sMCI, 70 cases (45.45%) by CDR, and 55 cases (32.16%) were clinically confirmed. 2) Taking clinical diagnosis as reference standard, the ROC curve showed CDR score had the highest accuracy in screening MCI, and the AUC was 0.90 ± 0.03 (95%CI: 0.844-0.957, P=0.000); the AUC of MoCA score was 0.53 ± 0.05 (95%CI: 0.430-0.621, P=0.603); when the cut⁃off value of sMCI score was 23, the AUC was 1.00 ± 0.00 (95%CI: 1.000-1.000, P=0.000). The cut⁃off value of subjects with education level of 0-3 years was adjusted to 22, and the AUC was 0.67 ± 0.05 (95%CI: 0.578-0.756, P=0.001). 3) According to education level, they were divided into 0-3 years group (113 cases) and 4-6 years group (47 cases). Taking CDR score as the reference standard, ROC curve showed the AUC of MoCA score in screening MCI in 4-6 years group was 0.49 ± 0.17 (95%CI: 0.157-0.824, P=0.955), the cut⁃off value of sMCI score was 23, the AUC of sMCI score was 0.50 ± 0.17 (95%CI: 0.161-0.839, P=1.000); the AUC of MoCA score in the 0-3 years group was 0.51 ± 0.06 (95%CI: 0.402-0.617, P=0.858), and the cut⁃off value of sMCI score was adjusted to 22, and the AUC was 0.64 ± 0.05 (95%CI: 0.535-0.744, P=0.011). Conclusions It is more common for the elderly in the rural communities with low education to have MCI, the accuracy of sMCI in screening MCI is higher than MoCA, and the cut⁃off value is 23 (education level 4-6 years) and 22 (education level 0-3 years), which is worthy of clinical application.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".