The use of hippocampal grading as a biomarker for early MCI
Bibliographic record
Abstract
Abstract INTRODUCTION Changes in the hippocampus are associated with both increased age and cognitive decline due to mild cognitive impairment (MCI) and Alzheimer’s disease (AD). Most studies have examined the association between hippocampal changes and episodic memory, with many reporting a relationship between hippocampal measurements and global cognition. However, these studies often find associations only in the later stages of cognitive decline. The goal of this study was to examine if hippocampal grading is associated with global cognition in cognitively normal controls (NC), early MCI (eMCI), late (lMCI), and AD, and whether such associations differ across diagnostic cohorts. METHODS Data from 1620 Alzheimer’s Disease Neuroimaging Initiative older adults were examined in this study (495 NC, 262 eMCI, 545 lMCI, and 318 AD). Participants were included if they completed baseline MRI scans and the Alzheimer’s disease Assessment Scale (ADAS-13) and Clinical Dementia Rating – Sum of Boxes (CDR-SB) cognitive tests. Linear regressions examined the influence of hippocampal grading on cognitive scores. RESULTS Lower global cognition (i.e., increased ADAS-13 scores) was associated with hippocampal grading scores in all cohorts, including normal controls. Lower global cognition (i.e., increased CDR-SB scores) was associated with hippocampal grading scores in lMCI and AD, but not in eMCI or NC groups. DISCUSSION These findings suggest that hippocampal grading is associated with changes in global cognition in NC, eMCI, lMCI, and AD depending on the cognitive test. Thus, hippocampal grading may be a useful measure that is sensitive to progressive changes early in the disease course.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".