Predictive ability of the Computer-Based Cognitive Assessment Tool for mild cognitive impairment in community-dwelling older adults: a 2-Year Longitudinal Study (Preprint)
Bibliographic record
Abstract
BACKGROUND The Computer-Based Cognitive Assessment Tool (CompBased-CAT) has been reported to have concurrent validity with the Mini-Mental State Examination (MMSE) and discriminating ability for dementia, but it was not clear whether it could predict incidence of cognitive impairment. OBJECTIVE This study examined the ability of the CompBased-CAT to predict mild cognitive impairment (MCI) after 2 years among community-dwelling older adults. METHODS A longitudinal study was conducted, involving 455 older adults (median age 72 years, range 65-89 years, 62.0% female) dwelling in communities. Cognitive function was assessed using the MMSE. MCI was defined as an MMSE score <27. The CompBased-CAT was conducted at baseline, and each sub-test score was converted to a Z-score, the sum of which became the total Z-score. Receiver Operating Characteristic (ROC) curve analysis was performed to determine the predictive ability of the CompBased-CAT for incidence of MCI. Logistic regression analysis was conducted with the dependent variable (the incidence of MCI), and with the total Z-score as the independent variable, adjusted for all other covariates. RESULTS After 2 years, 32 (7.0%) of participants developed MCI. ROC curve analysis showed an area under the curve of 0.79, a sensitivity of 0.76, and a specificity of 0.75. Logistic regression analysis showed that total Z-score was significantly associated with prevention of MCI. The odds ratio (OR) was 1.34 (95% confidence interval 1.18-1.52, p<.001). CONCLUSIONS The present study showed that CompBased-CAT has sufficient predictive ability for MCI 2 years later and that it is useful for identifying dementia at an early stage.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".