An Overview of New and Emerging Technologies for Early Diagnosis of Alzheimer Disease
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it