Amyloid imaging in Alzheimer’s disease: a potential new era of personalized medicine?
Bibliographic record
Abstract
Abstract Recent advances along clinical and neuropathological lines, as well as in our ability to detect the deposition of β-amyloid (Aβ) in vivo using positron emission tomography (PET), have helped redefine Alzheimer’s disease (AD) as a dynamic clinicobiological entity. On the basis of these advances, AD is now conceptualized as a continuum comprising asymptomatic, minimally symptomatic, and dementia phases, with detection of brain Aβ — in particular, via PET amyloid imaging — central to the diagnostic process. In this respect, [18F]florbetapir (Amyvid™) and [18F]flutemetamol (Vizamyl™) have recently received approval for clinical use from the Food and Drug Administration (FDA) and the European Medicines Agency (EMA), with additional radiofluorinated tracers for detection of Aβ in phase III trials. Recent initiatives such as the Alzheimer’s Disease Neuroimaging Initiative (ADNI) suggest that Aβ production, oligomerization and aggregation begins many years, possibly decades, before detectable cognitive impairment, with Aβ shown to associate with cognitive decline and conversion to dementia. While personalized medicine has now emerged as a prospect for the field, the recent decision by the Centers for Medicare & Medicaid Services (CMS) — who declined to cover the cost of amyloid PET imaging citing insufficient evidence to support its clinical utility — highlights that such a move may be premature.
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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 | 0.011 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".