Use of the Dementia Assessment Sheet for Community‐based Integrated Care System 21 items among Chinese community‐dwelling older adults
Bibliographic record
Abstract
AIM: The objective of this study was to verify the reliability and validity of the Dementia Assessment Sheet for Community-based Integrated Care System 21 items (DASC-21) among Chinese community-dwelling older adults, and to explore the related factors for dementia screening. METHODS: The study adopted a cross-sectional design, and a total of 1152 participants aged ≥60 years were recruited from 26 locations in China. All data were collected using questionnaires through face-to-face interviews. The logistic regression model was used to evaluate the effect factors of DASC-21 for dementia screening. The receiver operating characteristic curves were used to determine the optimal cut-off points and the accuracy of the DASC-21 for dementia and mild cognitive impairment screening. RESULTS: For test-retest reliability, the Pearson correlation coefficient was 0.873 (P < 0.001). In the criterion-related validity, the DASC-21 scores were significantly and negatively correlated with the Mini-Mental State Examination (r = -0.663, P < 0.001) and the Montreal Cognitive Assessment (r = -0.565, P < 0.001) scores. The results of the receiver operating characteristic analysis showed that there were different optimal cut-off values for different age groups. The areas under the receiver operating characteristic curves were 95.6% and 90.3% for dementia and mild cognitive impairment screening using DASC-21 after considering related effect factors. CONCLUSIONS: The DASC-21 was confirmed to be a valid and reliable instrument for dementia screening among Chinese community-dwelling older adults. Our results suggested that the age, education level and 2-week prevalence were important effect factors for dementia screening using the DASC-21. Geriatr Gerontol Int 2021; 21: 705-711.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".