Usability and Validity of a Battery of Computerised Cognitive Screening Tests for Detecting Cognitive Impairment
Bibliographic record
Abstract
BACKGROUND: Computerised cognitive screening (CCS) has the potential to detect cognitive impairment in the community, which is important for the early diagnosis of dementia. OBJECTIVE: The aim of this study was to investigate the ability of older adults with dementia to engage with smart phone and tablet technologies and to determine the accuracy of a battery of CCS tasks to detect cognitive impairment in comparison with the Montreal Cognitive Assessment (MoCA). METHODS: Patients with mild-moderate dementia (n = 40) attending a university-linked day hospital and normal controls (n = 20) completed (i) a questionnaire detailing the frequency and breadth of their technology use, (ii) three commercially available CCS tasks, and (iii) the MoCA. RESULTS: The three CCS tasks were completed by 85% (n = 34) of the patients with dementia and all controls; only 4 reported the task as 'hard'. Those with dementia scored significantly lower on the CCS than controls (p < 0.001). CCS scores correlated with total MoCA scores (r = 0.78, p < 0.01). Further, the CCS scores significantly predicted MoCA scores, controlling for the effects of age, gender, educational attainment, and frequency of technology use (β = 0.71, p < 0.001), explaining 65.2% of the variance. Total CCS and MoCA scores (cut-off score <24) had similar sensitivity (94 and 95%, respectively) and accuracy (area under the curve 0.94 and 0.99, respectively, p = 0.5) in discriminating dementia from controls, though the CSS had lower specificity (60 vs. 100% for the MoCA). CONCLUSION: The participants had little difficulty self-administering the CCS, which is an oft-cited barrier to computerised testing in this population. Our results support the criterion and construct validity of a CCS versus the commonly used MoCA. Although further research is required, CCS for cognitive impairment may be useful in the community and, by prompting referral to specialist services, could lead to an earlier diagnosis of dementia.
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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 | 0.007 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".