Framework for disclosing AD biomarker results following cognitive assessment within a pragmatic, multi‐country implementation evaluation
Bibliographic record
Abstract
Abstract Background Blood‐based biomarker tests are critical tools with the potential to change pathways for dementia diagnosis and care. These tests are less invasive than PET scan or lumbar puncture, potentially more affordable, and thus more accessible across more care settings and in more countries. Utilization in primary care settings together with brief cognitive assessments provides a compelling use case that could transform diagnosis, assessment and care. A critical challenge to the widespread adoption of blood‐based biomarker testing is the issue of disclosure. To date, Alzheimer’s Disease and Related Dementias (ADRD) biomarker disclosure recommendations have primarily focused on disclosure in research and/or specialized‐care settings. Primary care clinicians are well situated to conduct biomarker testing, but they are likely to need training and support to develop confidence and capacity to communicate results. Method Review of literature findings involving biomarker disclosure and input from dementia researchers from six countries reflecting diverse health care systems informed development of a disclosure framework to support the inaugural project of the Davos Alzheimer’s Collaborative’s (DAC) Systems Preparedness (DAC‐SP) initiative. This project seeks to promote routine uptake of cognitive screening among people ≥ age 60 and, when cognitive impairment is identified, blood‐based biomarker testing. The biomarker testing is completed using a commercially available, CLIA‐certified test that measures the likelihood of amyloid pathology when cognitive impairment is identified. Result The framework recognizes that biomarker disclosure is part of a comprehensive patient evaluation that includes cognitive assessment and a broader discussion of the patient’s health and wellbeing. It divides disclosure into five phases: pre‐test education; consent for and administration of testing; disclosure of results and discussion of treatment plan; provision of educational materials; and follow up (Table 1). This general framework can be adapted for site‐specific factors such as health literacy, health systems operations, legal structures, and cultural relevance. Conclusion As part of a pragmatic multisite implementation evaluation of a screening and early detection program including high and middle income countries, the authors have developed a framework to guide biomarker disclosure to patients in primary care settings. This provisional framework represents an important first step towards clinical consensus building on biomarker disclosure.
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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 | 0.570 | 0.409 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.009 | 0.009 |
| Scholarly communication | 0.018 | 0.015 |
| Open science | 0.011 | 0.026 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 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".