Glucagon-like peptide-1 analogs for Alzheimer’s disease -- A systematic meta-analysis
Bibliographic record
Abstract
Alzheimer’s Disease (AD) poses a serious health concern especially for the aging population above the years of 65. An estimated 6 million Americans are diagnosed with Alzheimer’s Disease, and there are at least 50 million Alzheimer’s patients in the world. AD affects the daily life of these patients, yet there is no permanent cure. Current treatments involve cholinesterase inhibitors and NMDA-receptor agonists to help alleviate the symptoms. GLP-1 is a peptide often used in the treatment of diabetes. Since there are shared pathological features between diabetes and AD, such as insulin dysfunction and glucose metabolism dysregulation, GLP-1 may be a viable study for AD treatment. To perform a meta-analysis to investigate whether GLP-1 has a beneficiary effect on biological markers and cognitive outcome in AD patients. We searched the following electronic databases: EMBASE, MEDLINE, phycINFO, CINAHL, PubMed, Cochrane CENTRAL, and ClinicalTrials.gov. We only utilized Randomized Control Trials (RCTs) and clinical trials. We also searched with the following Medical Search Headings: Alzheimer’s Disease, Alzheimer, Alzheimer’s, and GLP-1. We included 2 randomized, double-blind, and placebo controlled clinical trials into our meta-analysis. We extracted the baseline and outcomes from the clinical trials and evaluated its risks of bias. Biological markers were measured by amyloid beta (Aß) accumulation, and cognitive outcomes were measured by the Wechsler Memory Scale - Fourth Edition (WMS-IV) and Mini Mental State Exam (MMSE). For one study, the WMS-IV was used to measure cognitive outcome. The other study measured cognitive outcome with the MMSE. Biological markers were measured by Aß accumulation in one study and with [11C]PIB tracer in another. There was no significant difference between the placebo and experimental group after the treatment period.
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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 | 0.012 | 0.019 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.049 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 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".