Patient journey and decision processes for anti-amyloid therapy in Alzheimer’s disease
Bibliographic record
Abstract
INTRODUCTION: We utilized the Veterans Affairs Healthcare System administrative database to study the clinical decision-making processes for anti-amyloid therapy (AAT). METHODS: Patients with clinical notes mentioning lecanemab were identified (March 2023-June 2024) for manual review and structured database queries. RESULTS: From an initial sample (N = 2499), 1064 patients (55,000 notes) were reviewed manually (mean age 76 years; 7.3% women; 9.2% Black; 3.9% Hispanic). The AAT group (n = 56) had lower rates of common comorbidities, except post-traumatic stress disorder, than patients excluded from AAT (n = 528). The documented notes including "Lack of patient interest/resource constraints" (24.6% vs 3.6%), "anticoagulant use" (23.1% vs 10.7%), and "advanced AD" (18.6% vs 0), supplied partial explanations on exclusion vs inclusion. DISCUSSION: Only 5.3% of patients reached the point of care of being a candidate, scheduled for, or receiving AAT infusion. Patient preference and clinician discretion, especially regarding modifiable factors (e.g., medication regimens), appreciably influence the patient journey to AAT.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".