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 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 | 0.006 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".