Validation of a Computerized Cognitive Training Tool to Assess Cognitive Impairment and Enable Differentiation Between Mild Cognitive Impairment and Dementia
Bibliographic record
Abstract
Background: Age-related cognitive decline is a chronic, progressive process that requires active clinical management as cognitive status changes. Computerized cognitive training (CCT) provides cognitive exercises targeting specific cognitive domains delivered by computer or tablet. Meanwhile, CCT can be used to regularly monitor the cognitive status of patients, but it is not clear whether CCT can reliably assess cognitive ability or be used to diagnose different stages of cognitive impairment. Objective: To investigate whether CCT can accurately monitor the cognitive status of patients with cognitive impairment as well as distinguish patients with dementia from patients with mild cognitive impairment (MCI). Method: We included 116 patients (42 dementia and 74 MCI) in final analysis. Cognitive ability was assessed by averaging the patient performance on the CCT to determine the Cognitive Index. The validity of the Cognitive Index was evaluated by its correlation with neuropsychological tests, and internal consistency was measured to assess the reliability. Additionally, we determined the diagnostic ability of the Cognitive Index to detect dementia using receiver operating characteristic (ROC) analysis. Results: The Cognitive Index was highly correlated with the Montreal Cognitive Assessment (r = 0.812) and the Mini-Mental State Examination (r = 0.694), indicating good convergent validity, and the Cronbach’s alpha coefficient was 0.936, indicating excellent internal consistency. The area under the ROC curve, sensitivity, and specificity of the Cognitive Index to diagnose dementia were 0.943, 83.3%, and 91.9%, respectively. Conclusions: CCT can be used to assess cognitive status and detect dementia in patients with cognitive impairment.
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".