Predicting the incidence of mild cognitive impairment with a computer-based cognitive assessment tool in community-dwelling older adults: The Otassha study
Bibliographic record
Abstract
This study examined the ability of a computer-based cognitive assessment tool (CompBased-CAT) to predict mild cognitive impairment (MCI) in community-dwelling older adults. A two-year longitudinal study was conducted using data from 2016 to 2018 from the Otassha study cohort of community-dwelling older adults. MCI was defined as a Mini-Mental Status Examination (MMSE) score of <27. The CompBased-CAT was used at baseline, with each subtest score converted into a Z-score. Subsequently, the total Z-scores were calculated. Participants were divided into robust and MCI groups, and all variables were compared using the t-test or χ2 test. Receiver operating characteristic (ROC) curves and logistic regression analyses were conducted, with MCI and total Z-scores as dependent and independent variables, respectively. Among the 455 participants (median age, 72 years; range, 65-89 years; 282 women and 173 men), 32 developed MCI after two years. The participants in the MCI group were significantly older. They had lower maximal gait speed, baseline MMSE scores, subtest Z-scores, and total Z-scores than those in the robust group. The area under the ROC curve was 0.79 (95% confidence interval: 0.70-0.87; P <0.01). The sensitivity was 0.76, and the specificity was 0.75. The logistic regression analysis showed an odds ratio of 1.34 (95% confidence interval: 1.18-1.52; P <0.01). This study showed that CompBased-CAT can detect MCI, which is an early stage of dementia. Thus, CompBased-CAT can be used in future community health checkups and events for older adults.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".