Scalable biological-cognitive profiling for Alzheimer’s disease in the population
Bibliographic record
Abstract
Abstract Plasma phosphorylated tau217 has been suggested as a core biomarker for establishing a biological Alzheimer’s disease diagnosis. This blood biomarker has not been studied together with scalable cognitive assessment tools in population-based samples. We investigated the prevalence of cognitive and Alzheimer’s disease biomarker abnormalities and associations between plasma phosphorylated tau217 and remotely measured cognitive function in individuals without dementia. We used a population-based cross-sectional sample of 65–85-year-olds (n = 691, 57% females), excluding those with previously diagnosed Alzheimer’s disease or other dementia-causing neurodegenerative disease. Cognition was measured with a telephone-administered word list recall task (episodic memory) and animal naming (semantic fluency). Plasma phosphorylated tau217 was determined with the ALZpath assay. The prevalence of individuals with abnormalities in tests measuring episodic memory, semantic fluency, and plasma phosphorylated tau217 was 10–13%. Higher plasma phosphorylated tau217 levels were associated with lower scores on telephone-administered cognitive tests. We found a substantial minority of a population-based sample of individuals without a clinical diagnosis of Alzheimer’s disease to have cognitive and plasma phosphorylated tau217 profiles suggesting underlying Alzheimer’s disease. Combining plasma phosphorylated tau217 with remote cognitive assessment could be a scalable, accessible, and cost-effective protocol for screening individuals with undiagnosed or at risk for Alzheimer’s disease.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".