Analysis of Costs for Imaging-Assisted Pharmaceutical Intervention in Alzheimer’s Disease with Lecanemab: Snapshot of the First 3 Years
Bibliographic record
Abstract
BACKGROUND: The approval of lecanemab for the treatment of Alzheimer's disease (AD) by the Food and Drug Administration in the United States has sparked controversy over issues of safety, cost, and efficacy. Furthermore, the prognostication of cognitive decline is prohibitively difficult with current methods. The inability to forecast incipient dementia in patients with biological AD suggests a prophylactic scenario wherein all patients with cognitive decline are prescribed anti-AD drugs at the earliest manifestations of dementia; however, most patients with mild cognitive impairment (approximately 77.7%) do not develop dementia over a 3-year period. Prophylactic response therefore constitutes unethical, costly, and unnecessary treatment for these patients. OBJECTIVE: We present a snapshot of the costs associated with the first 3 years of mass availability of anti-AD drugs in a variety of scenarios. METHODS: We consider multiple prognostication scenarios with varying sensitivities and specificities based on neuroimaging studies in patients with mild cognitive impairment to determine approximate costs for the large-scale use of lecanemab. RESULTS: The combination of fluorodeoxyglucose and magnetic resonance was determined to be the most cost-efficient at $177,000 for every positive outcome every 3 years under an assumed adjustment in the price of lecanemab to $9,275 per year. CONCLUSIONS: Imaging-assisted identification of cognitive status in patients with prodromal AD is demonstrated to reduce costs and prevent instances of unnecessary treatment in all cases considered. This highlights the potential of this technology for the ethical prescription of anti-AD medications under a paradigm of imaging-assisted early detection for pharmaceutical intervention in the treatment of AD.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".