Primary care detection of Alzheimer’s disease using a self-administered digital cognitive test and blood biomarkers
Bibliographic record
Abstract
After the clinical implementation of amyloid-β-targeting therapies for people with cognitive impairment due to Alzheimer's disease (AD), there is an urgent need to efficiently identify this patient population in primary care. Therefore, we created a brief and self-administered digital cognitive test battery (BioCog). Based on its sub-scores, a logistic regression model was developed in a secondary care cohort (n = 223) and then evaluated in an independent primary care cohort comprising 19 primary care centers (n = 403). In primary care, BioCog had an accuracy of 85% when using a single cutoff to define cognitive impairment, which was significantly better than the assessment of primary care physicians (accuracy 73%). The accuracy increased to 90% when using a two-cutoff approach. BioCog had significantly higher accuracy than standard paper-and-pencil tests (that is, Mini-Mental State Examination, Montreal Cognitive Assessment, Mini-Cog) and another digital cognitive test. Furthermore, BioCog combined with a blood test could detect clinical, biomarker-verified AD with an accuracy of 90% (one cutoff), significantly better than standard-of-care (accuracy 70%) or when using the blood test alone (accuracy 80%). In conclusion, this proof-of-concept study shows that a brief, self-administered digital cognitive test battery can detect cognitive impairment and, when combined with a blood test, accurately identify clinical AD in primary care.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".