Comparison of Imaging Biomarkers in the Alzheimer Disease Neuroimaging Initiative and the Mayo Clinic Study of Aging
Bibliographic record
Abstract
OBJECTIVE: To determine whether magnetic resonance imaging measurements observed in the Alzheimer Disease Neuroimaging Initiative (ADNI) convenience sample differ from those observed in the Mayo Clinic Study of Aging (MCSA) population-based sample. DESIGN: Comparison of 2 samples. SETTING: Fifty-nine recruiting sites for the ADNI in the United States and Canada and the MCSA, a population-based cohort in Olmsted County, Minnesota. PATIENTS: Cognitively normal subjects and amnestic subjects with mild cognitive impairment were selected from the ADNI convenience cohort and MCSA population-based cohort. A simple random sample of subjects from both cohorts in the same age range was selected, and a second sample applied matching for age, sex, educational level, apolipoprotein E genotype, and Mini-Mental State Examination score. MAIN OUTCOME MEASURES: Baseline hippocampal volumes and annual percentage of decline in hippocampal volume. RESULTS: In the population-based sample, MCSA subjects were older, had less education, performed worse on the Mini-Mental State Examination, and had a family history of Alzheimer disease less often than did ADNI subjects. Baseline hippocampal volumes were larger in ADNI compared with MCSA cognitively normal subjects in the random sample, although no differences were observed after matching. Rates of decline in hippocampal volume were greater in the ADNI compared with the MCSA for cognitively normal subjects and those with amnestic mild cognitive impairment, even after matching. CONCLUSIONS: Rates of decline in hippocampal volume suggest that ADNI subjects have a more aggressive brain pathologic process than MCSA subjects and hence may not be representative of the general population. These findings have implications for treatment trials that use ADNI-like recruitment mechanisms and for studies validating new diagnostic criteria for Alzheimer disease in its various stages.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 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".