The Potential Emergence of Disease-Modifying Treatments for Alzheimer Disease: The Role of Primary Care in Managing the Patient Journey
Bibliographic record
Abstract
Despite recent setbacks, disease-modifying treatments (DMTs) for Alzheimer disease (AD) might become available within a few years. These DMTs are likely to be used in the early stages of AD to avoid the progression to manifest dementia, which implies that a large reservoir of prevalent cases would need to be evaluated when DMTs first become available. Primary care providers (PCPs) would play a vital role in managing the patient flow to specialty care. We review the literature on diagnostic tests that could be used by PCPs and estimate the impact of different testing approaches on demand for specialty care.While many tests have been evaluated, only the Mini-Mental State Examination (MMSE) and the Montreal Cognitive Assessment (MoCA) perform acceptably for detection of early-stage cognitive decline with sensitivities and specificities of 55% to 82% and 72% to 84%, respectively, for the MMSE; and 77% to 96% and 73% to 95%, respectively, for the MoCA. However, neither test is sufficiently specific for the AD pathology and would result in 4 to 5 false positives for each true positive. Blood-based tests for AD biomarkers may soon become available for clinical use. A plasma amyloid-β (Aβ) test has been shown to have a sensitivity of up to 97% and specificity of up to 81%. Adding this test to the MMSE or MoCA could reduce false positives by approximately 80%.These findings suggest a combination of brief cognitive tests and blood-based biomarker tests will allow PCPs to identify patients with potential early stage AD efficiently and triage them for further evaluation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".