Validity, Feasibility and Effectiveness of a Voice-recognition Based Digital Cognitive Screener for Dementia and Mild Cognitive Impairment in Community-dwelling Older Chinese Adults: A Large-scale Implementation Study
Bibliographic record
Abstract
To investigate the psychometric properties, administration efficiency and implementational feasibility of a previously piloted voice recognition- based digital cognitive screener for dementia detection in a large-scale community of elderly participants. Eligible participants completed the demographic, lifestyle investigations and the DCS. Domain-specific and global cognition was assessed by a comprehensive neuropsychological test battery. Diagnosis of mild cognitive impairment(MCI) and dementia was made based on the clinical dementia rating. Completion rate and administration time for the DCS were recorded. Correlation between the DCS and domain-specific and global cognitive performance were assessed. Receiver operating characteristic (ROC) analyses examined the discriminate validity of the DCS in detecting MCI and dementia. A cost-consequences analysis was conducted to compare the screening efficacy of DCS with two traditionally administered cognitive assessment tools, the Mini-Mental State Examination (MMSE) and the Montreal Cognitive Assessment (MoCA), was conducted. Among a total of 11,186 participants, the completion rate of the DCS was 97·5% with a conduction time of 5·6–6·1 minutes, regardless of gender, age and education stratifications. DCS total score was significantly associated with domain-specific and global cognitive z-scores. Area under the curves (AUCs) of the DCS were 0·95 (0·92, 0·99) and 0·83 (0·79, 0·88) for dementia and MCI detection, respectively. There was no significant difference on the AUCs among different age- and education-stratified subgroups. Comparing with the MoCA and MMSE, DCS resulted in time savings of 35·4%–36·0% and 30·7%–31·2% for identifying dementia cases, as well as 22·6%–22·8% and 16·2%–16·4% for identifying MCI cases. Our findings demonstrated that the DCS was an effective and efficient tool for case-finding of dementia and MCI in a Chinese community. The large-scale implementation of the DCS among older Chinese adults could be a practical cognitive screening strategy to improve the management of healthcare resources.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".