Neuropsychological testing and assessment for dementia
Bibliographic record
Abstract
This evidence-based review examines the utility of brief cognitive tests and neuropsychological testing (NPT) in the detection and diagnosis of mild cognitive impairment (MCI) and dementia. All patients presenting with cognitive complaints are recommended to have a brief screening test administered to document the presence and severity of memory/cognitive deficits. There is fair evidence to support the use of a range of new screening tests that can detect MCI and mild dementia with higher sensitivity (>or=80%) than the Mini-Mental State Exam (MMSE). NPT should be part of a clinically integrative approach to the diagnosis and differential diagnosis of dementia. It should be applied selectively to address specific clinical and diagnostic issues including: 1) The distinction between normal cognitive functioning in the aged, MCI and early dementia: there is fair evidence that NPT can document the presence of specific diagnostic criteria and provide additional useful information on the pattern of memory/cognitive impairment. 2) The evaluation of risk for Alzheimer disease (AD) or other types of dementia in persons with MCI: there is fair evidence that NPT measures or profiles can predict progression to dementia (predictive accuracy ranges from approximately 80 to 100%, sensitivities from 53 to 80%, and specificities from 67 to 99%). 3) DIFFERENTIAL DIAGNOSIS: There is fair evidence that NPT can complement clinical history and neuroimaging in determining the dementia etiology. Different dementia types have distinguishable NPT profiles though these may be stage-dependent, and increased sensitivity may be at the expense of specificity. 4) When NPT is part of a comprehensive assessment, which also entails clinical interviews and consideration of other clinical data, there is good evidence that it can contribute to management decisions in MCI and dementia, including the determination of retained and impaired cognitive abilities, their functional and vocational impact, and opportunities for cognitive rehabilitation.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".