P.008 Alzheimer’s disease biomarker testing from the perspective of patients and caregivers
Bibliographic record
Abstract
Background: To enrich our understanding of the impact of Alzheimer’s disease (AD) CSF biomarker testing on patients and caregivers, we examined these perspectives within the IMPACT-AD BC study. Methods: IMPACT-AD BC (NCT05002699, impactAD.org) is an observational, longitudinal study examining the impact of AD CSF biomarker testing (i.e., amyloid-beta and tau proteoforms) on personal and medical utility, and health economics. Patients underwent AD biomarker testing as part of medical care (n=142), and for the personal utility arm, a subset of patients (n=34), and their ‘care partner’ (n=31), were interviewed post-biomarker disclosure to understand their decision-making to undergo testing and the impact of learning the test results. Results: The primary consideration in patients’ decision to undergo testing was the desire for diagnostic clarity (63%). After biomarker result disclosure, patients’ positive feelings stemmed largely from having greater diagnostic certainty (55%) and the ability to plan for the future (23%), including making financial changes (58%) and care plans (21%). Care partners conveyed that biomarker testing provided needed information to help plan for the future and spurred them to connect with community resources. Conclusions: Patients and care partners value the diagnostic clarity from AD biomarker testing and use the information to make informed future plans.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.041 | 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".