A Neuropsychological Approach to Mild Cognitive Impairment
Bibliographic record
Abstract
OBJECTIVE: A neuropsychological approach to the detection and classification of mild cognitive impairment (MCI) using "gold standard" clinical ratings (CRs) was examined in a sample of independently functioning community dwelling seniors. The relationship between CRs and life satisfaction, concurrent validity of cognitive screening measures, and agreement between CRs and existing criteria for MCI were also determined. METHOD: One hundred and forty-two participants, aged 75 years and older, were administered a comprehensive battery of neuropsychological tests, along with self-report measures of psychological and psychosocial functioning, and functional independence. CRs were based on demographically corrected neuropsychological variables. RESULTS: The prevalence of MCI identified using CRs in this sample was 26.1%. Single and multiple domain subtypes of MCI were readily identified with subtypes reflecting Amnestic and Executive Function impairment predominating. Executive Function was a significant predictor of Life Satisfaction. The MoCA and MMSE both showed weak performance in detecting MCI based on CRs. There was substantial agreement between CRs and the classification criteria for MCI defined by Petersen/Winblad and Jak/Bondi. A global deficit score had near perfect performance as a proxy for CRs in detecting MCI in this sample. CONCLUSIONS: The results provide strong support for the utility of neuropsychological CRs as a "gold standard" operational definition in the detection and classification of MCI in 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".