MCI‐to‐normal reversion using neuropsychological criteria in the Alzheimer's Disease Neuroimaging Initiative
Bibliographic record
Abstract
INTRODUCTION: The low mild cognitive impairment (MCI) to cognitively normal (CN) reversion rate in the Alzheimer's Disease Neuroimaging Initiative (2-3%) suggests the need to examine reversion by other means. We applied comprehensive neuropsychological criteria (NP criteria) to determine the resulting MCI to CN reversion rate. METHODS: Participants with CN (n = 641) or MCI (n = 569) were classified at baseline and year 1 using NP criteria. Demographic, neuropsychological, and Alzheimer's disease biomarker variables as well as progression to dementia were examined across stable CN, reversion, and stable MCI groups. RESULTS: NP criteria produced a one-year reversion rate of 15.8%. Reverters had demographics, Alzheimer's disease biomarkers, and risk-of-progression most similar to the stable CN group and showed the most improvement on neuropsychological measures from baseline to year 1. DISCUSSION: NP criteria produced a reversion rate that is consistent with, albeit modestly improved from, reversion rates in meta-analyses. Reverters' biomarker profiles and progression rates suggest that NP criteria accurately tracked with underlying pathophysiologic status.
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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 | 0.010 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| 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 | 0.000 | 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".