An Overview of New and Emerging Technologies for Early Diagnosis of Alzheimer Disease
Bibliographic record
Abstract
Alzheimer disease is a progressive neurologic condition that leads to the decline of cognitive functioning and eventual death. There is currently no cure. Proposed causes of Alzheimer disease include the amyloid hypothesis, which suggests that it is caused by a buildup of amyloid-beta and tau proteins in the brain, leading to cell death. Recent diagnostic tools focus on amyloid and tau proteins as potential markers of the disease, and new treatments are also focusing on amyloid and tau formation. Earlier diagnosis of Alzheimer disease allows time for planning for care and support needs before symptoms worsen. It also allows for both drug and non-drug treatments to be used earlier, which may prolong time with a higher quality of life. Emerging diagnostic tools include biomarker-based tools, such as MRI, PET, CT, blood-based biomarkers, cerebrospinal fluid-based biomarkers, ocular testing, and salivary biomarkers. The majority of these tools are in the research phase, although imaging is often used in combination with cognitive testing to diagnose Alzheimer disease. One blood-based biomarker test is available in the US (paid out of pocket). It is unclear whether testing will be available in Canada or when this will happen.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".