Anti-Aβ agents for mild to moderate Alzheimer's disease: systematic review and meta-analysis
Bibliographic record
Abstract
Objective To assess the efficacy and safety of Aβ-targeting agents for mild to moderate Alzheimer’s disease. Methods The MEDLINE, Embase, Cochrane Central Register of Controlled Trials, PsycINFO, ClinicalTrials.gov and the WHO’s International Clinical Trials Registry Platform search portal were searched from their inception to April 2020. We generated pooled estimates using random effects meta-analyses. Results Nineteen randomised controlled trials, of which 17 had a low risk of bias, included 12 903 participants. The meta-analysis showed no difference in the cognitive subscale of Alzheimer’s Disease Assessment Scale (ADAS-Cog) between anti-Aβ drugs and placebo (mean difference (MD): 0.20, 95% CI −0.40 to 0.81; I 2 =99.8%; minimal important difference 3.1–3.8 points, moderate-certainty evidence). For ADAS-Cog, results suggested that one drug that increases Aβ clearance may differ in effect (MD: −0.96, 95% CI −0.99 to −0.92) from drugs that reduce Aβ production (MD: 0.78, 95% CI 0.25 to 1.32) (interaction p<0.000001); this difference also existed in the outcome of MMSE and CDR-SOB. Compared with placebo, anti-Aβ drug-related adverse events were as follows: anxiety, depression, diarrhoea, fatigue, rash, syncope and vomit. Discussion From current evidence, anti-Aβ interventions are unlikely to have an important impact on slowing cognitive or functional decline. Although the subgroup analysis suggested possible benefits from Aβ clearance drugs, the analysis has limited credibility, and a benefit from drugs that increase clearance, if real, is very small. Trial registration number PROSPERO registration number CRD42019126272.
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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 | 0.018 | 0.033 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.028 | 0.042 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".