Cognitive/Functional Measures Predict Alzheimer’s Disease, Dependent on Hippocampal Volume
Bibliographic record
Abstract
OBJECTIVES: This study aimed to investigate the predictive value of cognitive/functional measures in combination with hippocampal volume (HCV) on the probability of conversion from mild cognitive impairment (MCI) to Alzheimer's disease (AD). METHODS: The Rey Auditory Verbal Learning Test for immediate memory, Mini-Mental State Examination, a functional assessment for independent daily activities and Alzheimer's Disease Assessment Scale were used as cognitive/functional measures and HCV as neuroimaging measure. Logistic regression and Cox proportional hazard analyses were used to explore the measures' predictive values for AD conversion and time to conversion. RESULTS: The probability of conversion from MCI to AD was associated with cognitive function, but this was moderated by HCV: higher at lower HCV and lower at higher HCV. General cognitive/functional measures were less predictive than immediate memory in predicting time to conversion to AD at small HCVs. CONCLUSION: Effectiveness of cognitive measures and subtle functional abnormality in predicting conversion from MCI to AD is dependent on HCV, thus combined evaluation should be considered. A combination of HCV and immediate memory appear to perform best in predicting time to conversion.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".