A ‘Framingham-like’ Algorithm for Predicting 4-Year Risk of Progression to Amnestic Mild Cognitive Impairment or Alzheimer’s Disease Using Multidomain Information
Bibliographic record
Abstract
BACKGROUND: There are no agreed-upon variables for predicting progression from unimpaired cognition to amnestic mild cognitive impairment (aMCI), or from aMCI to Alzheimer's disease (AD). OBJECTIVE: Use ADNI data to develop a 'Framingham-like' prediction model for a 4-year period. METHODS: We developed models using the strongest baseline predictors from six domains (demographics, neuroimaging, CSF biomarkers, genetics, cognitive tests, and functional ability). We chose the best predictor from each domain, which was dichotomized into more versus less harmful. RESULTS: There were 224 unimpaired individuals and 424 aMCI subjects with baseline data on all predictors, of whom 37 (17% ) and 150 (35% ) converted to aMCI and AD, respectively, during 4 years of follow-up. For the unimpaired, CSF tau/Aβ ratio, hippocampal volume, and a memory score predicted progression. For those aMCI at baseline, the same predictors plus APOE4 status and functional ability predicted progression. Demographics and family history were not important predictors for progression for either group. The fit statistic was good for the unimpaired-aMCI model (C-statistic 0.80) and very good for the aMCI-AD model (C-statistic 0.91). Among the unimpaired, those with no harmful risk factors had a 4-year predicted 2% risk of progression, while those with the most harmful risk factors had a predicted 35% risk. The aMCI subjects with no harmful risk factors had a predicted 1% risk of progression those with all six harmful risk factors had a predicted 90% risk. CONCLUSION: Our parsimonious model accurately predicted progression from unimpaired to aMCI with three variables, and from aMCI to AD with five variables.
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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 | 0.011 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".