Evaluating Congruence Between Clinical and Biological Staging Across The Alzheimer’s Disease Spectrum
Bibliographic record
Abstract
BACKGROUND: The newly proposed criteria by the AA working group incorporates both biological and clinical stages to characterize the progression of AD. In this study, we aim to evaluate the agreement between these two complementary systems. METHODS: Using 188 participants from McGill TRIAD and 139 from the HEAD cohorts, we categorized participants into biological (0-4) and clinical (0-4) stages using amyloid PET, tau PET(MK-6240), and clinical measures as described by the working group. Participants were then stratified into three categories: congruent (clinical = biological), higher in the biological stage (clinical < biological), and higher in the clinical stage (clinical > biological). In the TRIAD cohort, we further compared these groups for age, sex, years of education, vascular burden (WMH), microglial activation (PBR scan), Astrocyte reactivity (GFAP), amyloid load, tau load, and NPI-Q scores using Welch two-sided t-test with FDR multiple comparison correction. RESULTS: In the TRIAD cohort, 34% of participants were congruent, 24.5% had a higher clinical stage, and 41.5% had a higher biological stage. Meanwhile in the HEAD cohort 68.2% were congruent, 20.7% had a higher clinical stage, and 17% had a higher biological stage. TRIAD participants with a higher clinical stage had lower education (P = 0.02), more neuropsychiatric symptoms (P = 0.03), and a higher vascular burden (P = 0.03) (Table 1). As expected, people with higher biological stage had more amyloid(P<0.001), tau(P<0.001), and astrocyte reactivity(P<0.001) (Table 1, Figure 2). CONCLUSIONS: Our findings highlight important discordance between clinical and biological stages, which could be partially explained by cognitive reserve. This was supported by the protective effects of educational attainment in participants with a higher biological stage. Vascular burden played a major role in the cognitive impairment of individuals with higher clinical stages. Future studies should replicate these findings in larger more representative population-based cohorts.
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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 | 0.013 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".