Bibliographic record
Abstract
Lecanemab is a novel drug developed by Sisai and Biogen aimed at treating Alzheimer’s Disease. Alzheimer’s Disease is characterized by the misfolding and accumulation of neurotoxic amyloid-beta plaques in the brain, leading to neuronal cell death and brain shrinkage. Lecanemab contains monoclonal antibodies that have a high affinity for soluble and insoluble forms of amyloid-beta peptides. These antibodies bind, neutralize, and eliminate amyloid-beta aggregates to slow Alzheimer’s Disease progression.2 Phase II dose-finding trial for Lecanemab conducted with 854 participants with early Alzheimer’s Disease demonstrated no significant difference between Lecanemab and placebo groups at the 12-month mark. However, a 27% reduction in clinical decline in the Lecanemab group was seen after 18 months compared to the placebo group. Adverse effects of Lecanemab include transient fever and asymptomatic fluid formation in the brain. The drug was approved by the Food and Drug Administration and launched in the United States on January 18, 2023. Manufacturing and marketing applications for Lecanemabwere also recently submitted in Europe, Japan, and Canada1 With FDA approval, the drug is currently priced at 26,500 USD per year, with Medicare pledging to cover 80% of the cost.3
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.010 |
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".