Discriminative accuracy of the A/T/N scheme to identify cognitive impairment due to Alzheimer's disease
Bibliographic record
Abstract
Abstract Introduction The optimal combination of amyloid‐β/tau/neurodegeneration (A/T/N) biomarker profiles for the diagnosis of Alzheimer's disease (AD) dementia is unclear. Methods We examined the discriminative accuracy of A/T/N combinations assessed with neuroimaging biomarkers for the differentiation of AD from cognitively unimpaired (CU) elderly and non‐AD neurodegenerative diseases in the TRIAD, BioFINDER‐1 and BioFINDER‐2 cohorts (total n = 832) using area under the receiver operating characteristic curves (AUC). Results For the diagnosis of AD dementia (vs. CU elderly), T biomarkers performed as well as the complete A/T/N system (AUC range: 0.90–0.99). A and T biomarkers in isolation performed as well as the complete A/T/N system in differentiating AD dementia from non‐AD neurodegenerative diseases (AUC range; A biomarker: 0.84–1; T biomarker: 0.83–1). Discussion In diagnostic settings, the use of A or T neuroimaging biomarkers alone can reduce patient burden and medical costs compared with using their combination, without significantly compromising accuracy.
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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 | 0.012 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".