Use of amyloid PET across the spectrum of Alzheimer’s disease: clinical utility and associated ethical issues
Bibliographic record
Abstract
Abstract Recent advances have made possible the in vivo detection of beta-amyloid (Aβ) pathology using positron emission tomography. While the gold standard for amyloid imaging, carbon-11 labeled Pittsburgh compound B is increasingly being replaced by fluorine-18 labeled radiopharmaceuticals, with three already approved for clinical use by US and European regulatory bodies. Appropriate use criteria proposed by an amyloid imaging taskforce convened by the Alzheimer's Association and the Society of Nuclear Medicine and Molecular Imaging recommend restricting use of this technology to the evaluation of patients with mild cognitive impairment or atypical dementia syndromes. While use among asymptomatic individuals is currently viewed as inappropriate due prognostic uncertainty, elevated levels of brain Aβ among asymptomatic individuals may represent preclinical Alzheimer's disease. Amyloid imaging is likewise expected to play a role in the design of clinical trials. Though preliminary results suggest amyloid imaging to possess clinical utility and cost-effectiveness, both domains have yet to be assessed systematically. As the field moves toward adoption of a pro-disclosure stance for amyloid imaging findings, it is imperative that a broad range of stakeholders be involved to ensure the appropriateness of emerging policies and protocols.
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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 | 0.016 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".