Early versus late MCI: Improved MCI staging using a neuropsychological approach
Bibliographic record
Abstract
INTRODUCTION: The Alzheimer's Disease Neuroimaging Initiative (ADNI) separates "early" and "late" mild cognitive impairment (MCI) based on a single memory test. We compared ADNI's MCI classifications to our neuropsychological approach, which more broadly assesses cognitive abilities. METHODS: Three hundred thirty-six ADNI-2 participants were classified as "early" or "late" MCI. Cluster analysis was performed on neuropsychological test data, and participants were reclassified based on cluster results. These two staging approaches were compared on progression rates, cerebrospinal fluid biomarkers, and cortical thickness profiles. RESULTS: There was little correspondence between the two staging methods. ADNI's early MCI group included a large proportion of false-positive diagnostic errors. The reclassified neuropsychological MCI groups showed steeper survival curves and more abnormal biomarkers. CONCLUSIONS: Our novel neuropsychological approach improved the staging of MCI by (1) capturing individuals at an early symptomatic stage, (2) minimizing false-positive cases, and (3) identifying a late MCI group further along the disease trajectory.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".