The Future of AD Clinical Trials with the Advent of Anti-Amyloid Therapies: An CTAD Task Force Report
Bibliographic record
Abstract
BACKGROUND: Aducanumab (ADUHELMTM) was approved for the treatment of Alzheimer's disease (AD) in the US. This approval was supported by an effect on the cerebral amyloid plaque load and evidence of cognitive efficacy to be confirmed in post-marketing trials. Other anti-amyloid antibodies are under investigation in phase III (donanemab, lecanemab, gantenerumab) and have shown preliminary evidence of a cognitive benefit in phase II trials. Although these agents target a small segment of patients with mild cognitive impairment due to AD or mild AD dementia, their advent will change the design of future clinical trials both for anti-amyloid and non-amyloid drugs. These changes will promote the selection of patients in clinical trials by amyloid and tau biomarkers that identify patients with appropriate biology and may follow the treatment response to approved amyloid antibodies. The use of these agents creates the opportunity to test combined drug therapies and to conduct comparative assessments with innovative therapies and newly approved drugs available in clinical practice. Blood-based AD biomarkers should be implemented in research and could facilitate the recruitment into clinical trials. Anti-amyloid antibodies will have positive (e.g., more early diagnosis) and negative impacts (some subjects will be reluctant to participate in trials and risk assignment to placebo) on AD trials in the immediate future. We present the results of the CTAD Task Force on this topic, in Boston, November 6, 2021.
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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 | 0.318 | 0.217 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.020 | 0.006 |
| Open science | 0.009 | 0.008 |
| Research integrity | 0.043 | 0.022 |
| Insufficient payload (model declined to judge) | 0.007 | 0.008 |
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".