A Prospective Longitude Study of Lecanemab in Early Alzheimer's Disease: 6‐month follow up
Bibliographic record
Abstract
BACKGROUND: Alzheimer's disease (AD) prevalence is expected to rise dramatically due to rapidly aging populations. Alzheimer's disease pathology is triggered by the accumulation of soluble and insoluble aggregated Aβ peptides (oligomers, protofibrils, and fibrils). Lecanemab is an IgG1 monoclonal antibody, preferentially targets soluble aggregated amyloid beta (Aβ), with activity across oligomers, protofibrils, and insoluble fibrils. In the recent study, lecanemab demonstrated a consistent slowing of reduction in brain amyloid in early AD. This study aims to assess lecanemab in early AD, mild cognitive impairment due to AD and mild AD dementia. METHOD: Eligible patients were treated with lecanemab (10 mg/kg biweekly). The primary efficacy endpoint in the core study was change in the Alzheimer's disease assessment scale-cognitive section(ADAS), Minimum Mental State Examination(MMSE) and Montreal Cognitive Assessment(MoCA) from baseline at 6 months. Key secondary endpoints included change from baseline at 6 months in amyloid PET Centiloids (in patients participating in the amyloid PET sub-study). RESULT: A total of 50 subjects were treated with lecanemab, among whom 10 completed a 6-month follow-up. For the primary endpoint, there was a slowing of decline with lecanemab in cognitive at 6 months compared to baseline. For the primary endpoint, 10-mg/kg biweekly lecanemab reduced brain amyloid in frontal cortex (-0.469 SUVr units), lateral parietal cortex (-0.361 SUVr units), lateral temporal lobe cortex (-0.374 SUVr units), medial temporal lobe cortex(-0.437 SUVr units), rear clasp back (-0.420 SUVr units), precuneus (-0.489 SUVr units), occipital cortex (-0.393 SUVr units) and cerebellar cortex (-0.174SUVr units), but it's not significant(p > .05). CONCLUSION: Our findings analyses demonstrated reduction in brain amyloid accompanied by a consistent reduction of clinical decline across several clinical and biomarker endpoints.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".