Cognitive assessment with neurocognitive screening tools
Bibliographic record
Abstract
Neurocognitive Disorders (NCDs) are widespread diseases, especially in elderly. The future possibility of having more effective treatments has to deal with the lack of early disorder detection, which would enable patients to benefit the most from them. Individual medical history, blood and cerebrospinal fluid tests, and neuroimaging, among other tools, support the diagnostic process; neurocognitive screening tests also have an irreplaceable role to play in NCD assessment. Ease of execution and low cost make these instruments to be so appreciate in clinical practice. Mini Mental State Examination (MMSE), Repeatable Battery for the Assessment of Neuropsychological Status (RBANS), Montreal Cognitive Assessment (MoCA), and Clock Drawing Test (CDT), used as first-level screening tools, will be at the centre of this mini review. In the present work we will highlight the contribution made by these tests in NCD assessment, emphasizing some interesting aspects recently emerged from the scientific literature.
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How this classification was reachedexpand
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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".