Real-world experience with baseline characteristics and safety of lecanemab for Alzheimer's disease in Eastern China
Bibliographic record
Abstract
Background Lecanemab reduces amyloid levels and modestly slows cognitive decline in a large cohort of early Alzheimer's disease (AD) but lacks real-world safety data in Chinese population. Objective The real-world study aims to analyze baseline characteristics and preliminary safety of lecanemab for AD in Zhejiang Province, and to evaluate the efficacy of plasma biomarkers for patient screening. Methods This multi-center study included 190 patients with AD in Zhejiang Province, who completed baseline assessments and received lecanemab treatment with follow-up. Results The study included 176 participants with early AD and 14 moderate. In the early AD (mean age 68.04 years, Mini-Mental State Examination 20.03 and Montreal Cognitive Assessment 14.93), 124 (70.5%) participants were female, and 127 (72.1%) were junior high school education level or less. APOE4 heterozygote was predominant (48.9%). Logistic regression for distinguishing early AD from the Aβ negative cognitively unimpaired populations showed that p-Tau 217 independently provided better classification efficacy (area under the curve = 0.9983, p < 0.0001). In the early AD, 29 (16.5%) participants experienced infusion-related reactions (IRR) after the first-dose lecanemab, and amyloid-related imaging abnormalities (ARIA) were identified in 17 patients (9.7%), while 3 (21.4%) with IRR and none ARIA observed in the moderate AD. Conclusions The real-world lecanemab cohort had more females, lower educational level, and higher disease burden compared with the clinical trial cohort. Overall lecanemab exhibited a manageable short-term safety profile with no measurable cognitive efficacy. Extensive monitoring and management are required for ARIA of clinically importance. The plasma p-Tau 217 showed high accuracy for early AD screening.
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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 | 0.003 |
| 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) | 0.001 | 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".