Independent Predictors of Cognitive Decline in Healthy Elderly Persons
Bibliographic record
Abstract
BACKGROUND: Several studies have shown that individually memory, hippocampal volume, and motor measures presage the onset of dementia. It is unclear if these independently contribute to the prediction of mild cognitive impairment. OBJECTIVE: To determine the ability of memory, hippocampal volume, and a gait speed to independently predict cognitive decline in healthy elderly persons. DESIGN: A prospective, longitudinal, observational cohort study with a mean follow-up of 6 years. PARTICIPANTS: One hundred eight optimally healthy elderly cognitively intact subjects. MAIN OUTCOME MEASURES: Any cognitive impairment noted on the Clinical Dementia Rating Scale (score = 0.5) or persistent or progressive cognitive impairment. Cox modeling determined if time to onset of cognitive impairment was associated with baseline logical memory II test score (a measure of delayed recall), hippocampal volume (magnetic resonance imaging), or gait speed (time to walk 30 ft [9 m]) independent of age, sex, depression, or the allele producing the epsilon4 type of apolipoprotein E (APOE epsilon4). RESULTS: Questionable dementia occurred in 48 participants in a mean (SD) of 3.7 (2.4) years. This progressed to persistent cognitive impairment in 38 of these participants in a mean (SD) of 4.4 (2.4) years. Logical memory II test performance and hippocampal volume each predicted onset of questionable dementia, independent of age and sex. Time to walk 30 ft additionally contributed independently to the prediction of time to onset of persistent cognitive impairment. Possessing the APOE epsilon4 allele and depression did not enter either model significantly. CONCLUSIONS: Models combining multiple risk factors should refine the prediction of questionable dementia and persistent cognitive impairment, harbingers of dementia. Individuals at risk for cognitive impairment may represent a high-risk group for intervention.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".