Detection of Early Alzheimer’s disease at AdventHealth: A Davos Alzheimer’s Collaborative flagship site
Bibliographic record
Abstract
Abstract Background Early identification of Alzheimer’s disease (AD) is critical for disease‐modifying therapies. The Davos Alzheimer’s Collaborative flagship program tested the feasibility of implementing a digital cognitive assessment (DCA) followed by a blood biomarker (BBM) for early detection of cognitive impairment (CI). Method Individuals ≥65 years without dementia were approached via their primary care provider (PCP) or through direct‐to‐consumer (DTC) social media. After consenting, participants completed the Cogstate Brief Battery (CBB) DCA. Participants with an abnormal or borderline CBB score were offered the Montreal Cognitive Assessment (MoCA) and the PrecivityAD® blood test, a CLIA‐certified laboratory developed test that uses mass spectrometry to analyze biomarkers to identify brain amyloid plaques (reported by the Amyloid Probability Score‐APS) in individuals with CI. Result Over 2300 participants expressed interest. Of 2001 eligible, 1076 (96% social media, 4% PCP) e‐consented. 742 completed the CBB of which 211 (28%) were borderline, 113 (15%) abnormal, and 418 (56%) were negative for CI. Of the 324 with borderline or abnormal CBB scores, 219 (67%) completed the MoCA (59% Normal range, 38% Mild CI range, 2% Moderate CI range, and 0.5% Severe CI range). Of the 324, 218 (67%) received BBM: 18.8% had High APS, 67.9% Low APS, and 13.3% Intermediate (e.g. non‐informative) APS. The APS result for participants with a normal MoCA showed 20% with High APS, 12% with an Intermediate APS, and 69% with Low APS. Of those with an impaired MoCA 18% had High APS, 15% Intermediate and 67% low APS. A Fisher’s Exact test determined there was no statistically significant relationship between MoCA impairment and APS category (p‐value 0.68). Conclusion This study highlights the success of a DTC approach. The MoCA, alone, is insufficient to identify risk of AD in individuals with CI. A more comprehensive clinical evaluation of AD can be enhanced with the addition of a BBM leading to better disease‐modifying strategies.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".