Theoretical impact of the AT(N) framework on dementia using a community autopsy sample
Bibliographic record
Abstract
The AT(N) research framework categorizes eight biomarker profiles using amyloid (A), tauopathy (T), and neurodegeneration (N), regardless of dementia status. We evaluated associations with dementia risk in a community-based cohort by approximating AT(N) profiles using autopsy-based neuropathology correlates, and considered cost implications for clinical trials for secondary prevention of dementia based on AT(N) profiles. We used Consortium to Establish a Registry for Alzheimer's Disease (moderate/frequent) to approximate A+, Braak stage (IV-VI) for T+, and temporal pole lateral ventricular dilation for (N)+. Outcomes included dementia prevalence at death and incidence in the last 5 years of life. A+T+(N)+ was the most common profile (31%). Dementia prevalence ranged from 14% (A-T-[N]-) to 79% (A+T+[N]+). Between 8% (A+T-[N]-) and 68% (A+T+[N]-) of decedents developed incident dementia in the last 5 years of life. Clinical trials would incur substantial expense to characterize AT(N). Many people with biomarker-defined preclinical Alzheimer's disease will never develop clinical dementia during life, highlighting resilience to clinical expression of AD neuropathologic changes and the need for improved tools for prediction beyond current AT(N) biomarkers.
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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 | 0.080 | 0.178 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".