Aducanumab - a potential pharmacological therapeutic treatments for Alzheimer’s disease
Bibliographic record
Abstract
The first cause of dementia symptoms in the elderly worldwide is Alzheimer's disease (AD), which lead to a continuous and gradual memory loss that causes significant distress towards people. Known from World Alzheimer’s Report, dementia is now the top 7 mortality cause globally. There are several hypothesis of AD pathogenesis. Among them, beta-amyloid cascade hypothesis and hyperphosphorylation of tau protein are two of the most mainstream opinions. However, beta-amyloid cascade hypothesis is being questioned. In order to deal with this disease, both non-pharmacological (cognitive improvement) and pharmacological therapeutics (rivastigmine) can relieve symptoms of AD, such as dementia to some extent, but they cannot directly treat AD. Under this situation, for the purpose of finding drugs that can cure AD, thousands of drugs clinical trials are under progress. Although most of the ongoing drugs passed through the phase 2 clinical trial successfully, only Aducanumab passed though the phase 3, becoming the first new drug for AD approved by FDA. Nonetheless, Aducanumab is controversial in the scientific community. This paper briefly introduces the mechanism of action and research progress of aducanumab.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.004 | 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".