Global multi‐specialty clinician perspectives on the implementation of Alzheimer's disease blood biomarkers
Bibliographic record
Abstract
INTRODUCTION: Clinicians' views on the clinical readiness of Alzheimer's disease (AD) blood biomarkers (BBMs) are not well understood. METHODS: The Alzheimer's Association International Society to Advance Alzheimer's Research and Treatment Biofluid-Based Biomarkers Professional Interest Area conducted a survey to elicit clinician opinions on AD BBM implementation, including contexts of use, assay selection, reporting, and result interpretation. RESULTS: Clinician respondents (n = 212) practiced in Europe (56%), North America (24%), the Caribbean and Central/South America (11%), and other continents (9%). Most respondents were medical doctors (80%) practicing in secondary or tertiary care (88%). For 56%, cerebrospinal fluid AD biomarkers or amyloid positron emission tomography were accessible, but 48% agreed and 52% disagreed with the implementation of AD BBMs in any clinical context. Respondents emphasized the need for data from diverse populations and educational resources to support test interpretation. DISCUSSION: Surveyed clinicians generally agreed with published appropriate use recommendations but were divided on AD BBM readiness for clinical use. HIGHLIGHTS: A survey of clinicians was conducted regarding clinical readiness of Alzheimer's disease (AD) blood biomarkers (BBMs). Views were split on AD BBM clinical readiness: 48% agreed, 52% disagreed. Most responders supported AD BBM use for treatment decisions. Most responders opposed AD BBM testing in asymptomatic individuals. Test performance data and educational materials to aid interpretation were of high importance.
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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 | 0.030 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".