S5‐02–03: Diagnosis of mild cognitive impairment in research and clinical settings
Bibliographic record
Abstract
Transitional cognitive states between normal cognition and mild dementia, such as Mild Cognitive Impairment (MCI), tend to progress, but at variable rates that may take a decade or more before the subject is demented. This progression to clinical AD is considered to be the best validation, albeit retrospective, of an AD diagnosis during life. Rates of progression to AD may be influenced by cerebro-vascular, psychiatric, and medical illnesses, social conditions and individual lifestyles. A history of progressive cognitive and functional impairment and objective deficits, especially of episodic memory, medial temporal atrophy (MTA) on MRI scans and the ApoEe4 genotype are all associated with the presence and severity of AD pathology in the brain and predict progression to AD. We have developed an algorithm for making a cognitive diagnosis that combines neuropsychological scores with a quantified version of the history, derived from a modification of the Clinical Dementia Rating Scale questionnaire, to classify subjects into the following cognitive diagnostic categories: (1) Probable Normal; (2) Possible Normal; (3) Possible MCI; (4) Probable MCI, (5) Possible Dementia; (6) Probable Dementia. The subject's MTA score and ApoE e4 allele number are combined to create a Composite AD Biomarker (CAB) score. The MTA score is an average of hippocampal, entorhinal and perirhinal cortex atrophy scores, measured on 1.5 mm thickness, coronal MRI slices, intersecting the mamillary bodies, with the visual rating guided by drop-down reference images. MTA and CAB scores were strongly predictive of the clinical diagnosis (normal, MCI or AD) as well as the algorithmic cognitive diagnoses (1- 6). CAB scores correlated with neuropsychological test scores, as well as measures of apathy, but not depression. Other correlations of CAB scores to measures of cognitive and physical activity and of motor performance will be presented. The MTA and CAB scores are correlated with clinical diagnosis and with clinical markers of AD. The combination of the CAB score and the algorithmic cognitive diagnosis may prove to be a useful method biomarker for diagnosing AD, among non-demented subjects and for evaluating the relationship of clinical features to the underlying pathology of AD.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.025 | 0.012 |
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".