Computer Assessment of Mild Cognitive Impairment
Bibliographic record
Abstract
Many older individuals experience cognitive decline with aging. The causes of cognitive dysfunction range from the devastating effects of Alzheimer's disease (AD) to treatable causes of dysfunction and the normal mild forgetfulness described by many older individuals. Even mild cognitive dysfunction can impact medication adherence, impair decision making, and affect the ability to drive or work. However, primary care physicians do not routinely screen for cognitive difficulties and many older patients do not report cognitive problems. Identifying cognitive impairment at an office visit would permit earlier referral for diagnostic work-up and treatment. The Computer Assessment of Mild Cognitive Impairment (CAMCI) is a self-administered, user-friendly computer test that scores automatically and can be completed independently in a quiet space, such as a doctor's examination room. The goal of this study was to compare the sensitivity and specificity of the CAMCI and the Mini Mental State Examination (MMSE) to identify mild cognitive impairment (MCI) in 524 nondemented individuals > 60 years old who completed a comprehensive neuropsychological and clinical assessment together with the CAMCI and MMSE. We hypothesized that the CAMCI would exhibit good sensitivity and specificity and would be superior compared with the MMSE in these measures. The results indicated that the MMSE was relatively insensitive to MCI. In contrast, the CAMCI was highly sensitive (86%) and specific (94%) for the identification of MCI in a population of community-dwelling nondemented elderly individuals.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".