Stratifying dementia risk factors: A prediction model and hypothesis‐driven analysis
Bibliographic record
Abstract
INTRODUCTION: Most older adults present multimorbidity, but dementia risk factors are typically analyzed individually. Direct methodological comparisons evaluating simultaneous multiple risk factors are essential to provide the realistic effects of multimorbidity. We aimed to compare hypothesis- and data-driven approaches for dementia risk stratification in a real-world cohort. METHODS: We analyzed 9606 participants from the National Alzheimer's Coordinating Center (NACC) Uniform Data Set (2005-2023) using machine learning with interpretability analysis and survival models to simultaneously evaluate 13 risk factors for incident dementia conversion. RESULTS: A total of 877 participants (9%) developed dementia over (mean ± SD, 6 ± 4.2) years of follow-up. Both approaches consistently identified four key predictors: age, depression, low education, and body mass index (protective). Convergent findings across methodologies demonstrated robust factor identification despite different analytical paradigms. DISCUSSION: In this direct methodological comparison, age, depression, and low education emerge as major dementia risk factors regardless of analytical approach. Convergent interpretability of these approaches support simultaneous multifactorial risk assessment in clinical practice. HIGHLIGHTS: Data- and hypothesis-driven approaches identified convergent key risk factors Age, depression, and low education are major risk factors for dementia Higher body mass index was unexpectedly protective against dementia conversion Multimorbidity requires simultaneous evaluation of multiple risk factors Real-world analysis reveals complex interactions between dementia risks.
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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 | 0.057 | 0.081 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".