Effect of reduction in brain amyloid levels on change in cognitive and functional decline in randomized clinical trials: An instrumental variable meta‐analysis
Bibliographic record
Abstract
INTRODUCTION: Whether the reduction in brain amyloid beta (Aβ) plaque alone may substantially slow cognitive and functional decline in patients with dementia or mild cognitive impairment due to Alzheimer's disease (AD) remains debated. METHODS: An instrumental variable meta-analysis was performed to infer the effect of change in positron emission tomography (PET)-measured Aβ standardized uptake value ratio (SUVR) on cognitive and functional decline. RESULTS: Pooling data from 16 randomized trials demonstrates that each 0.1-unit decrease in PET Aβ SUVR is associated with a reduction (95% confidence interval) by 0.09 (0.034-0.15), 0.33 (0.12-0.55), and 0.13 (0.017-0.24) point in the average change of the Clinical Dementia Rating-Sum of Boxes, the Alzheimer's Disease Assessment Scale-Cognitive Subscale, and the Mini-Mental State Examination, respectively. DISCUSSION: This meta-analysis provides statistically significant evidence of a likely causal relationship between a reduction in Aβ plaque and a reduction in cognitive and functional decline in patients with AD. HIGHLIGHTS: A widely cited meta-analysis article concluded amyloid beta reduction does not substantially improve cognition. We identified data inconsistencies in the initial publication and found new trial data. We repeated the meta-analysis after correcting data inconsistencies and adding new trial data. Updated results suggested statistically significant clinical benefit of amyloid beta reduction. Amyloid beta is a viable biological target for the treatment and prevention of AD.
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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 | 0.064 | 0.109 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.075 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".