Validity of a novel computerized cognitive battery for mild cognitive impairment
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: The NeuroTrax Mindstreams computerized cognitive assessment system was designed for widespread clinical and research use in detecting mild cognitive impairment (MCI). However, the capability of Mindstreams tests to discriminate elderly with MCI from those who are cognitively healthy has yet to be evaluated. Moreover, the comparability between these tests and traditional neuropsychological tests in detecting MCI has not been examined. METHODS: A 2-center study was designed to assess discriminant validity of tests in the Mindstreams Mild Impairment Battery. Participants were 30 individuals diagnosed with MCI, 29 with mild Alzheimer's disease (AD), and 39 healthy elderly. Testing was with the Mindstreams battery and traditional neuropsychological tests. Receiver operating characteristic (ROC) analysis was used to examine the ability of Mindstreams and traditional measures to discriminate those with MCI from cognitively healthy elderly. Between-group comparisons were made (Mann-Whitney U test) between MCI and healthy elderly and between MCI and mild AD groups. RESULTS: Mindstreams outcome parameters across multiple cognitive domains significantly discriminated among MCI and healthy elderly with considerable effect sizes (p < 0.05). Measures of memory, executive function, visual spatial skills, and verbal fluency discriminated best, and discriminability was at least comparable to that of traditional neuropsychological tests in these domains. CONCLUSIONS: Mindstreams tests are effective in detecting MCI, providing a comprehensive profile of cognitive function. Further, the enhanced precision and ease of use of these computerized tests make the NeuroTrax system a valuable clinical tool in the identification of elderly at high risk for dementia.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it