27,380 Alzheimer's and dementia studies in plain English, each scored out of 100.
We tested the trained model using five other studies, three of which used PET as a biomarker of significant amyloid β burden (testing the deep learning model between amyloid β positive vs amyloid β negative). 2 key findings →
Design: case-control study Participants: 648 Evidence: moderate
Full summary, score breakdown and related studies → Original source Free full text
Alzheimer's disease (AD) is a progressive neurodegenerative condition marked by a decline in cognitive functions with no validated disease modifying treatment. 1 key finding →
Design: cross-sectional Evidence: moderate
Full summary, score breakdown and related studies → Original source Free full text
Pathogenic mutations in APP, PSEN1, PSEN2, MAPT and GRN have previously been linked to familial early onset forms of dementia. 4 key findings →
Evidence: low
Full summary, score breakdown and related studies → Original source Free full text
The discovery cohort contained 3,671 individuals with a mean age of 68 years and consisted of 49% reported female. 5 key findings →
Participants: 3,671 Evidence: moderate
Full summary, score breakdown and related studies → Original source Free full text
Most approaches to machine learning from electronic health data can only predict a single endpoint. 1 key finding →
Participants: 1,909 Evidence: low
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JNPL3 transgenic mice expressing a mutant tau protein, which develop neurofibrillary tangles and progressive motor disturbance, were crossed with Tg2576 transgenic mice expressing mutant beta-amyloid precursor protein (APP), thus modulating the APP-Abeta (beta-amyloid peptide) environment.
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There has been recent interest in the application of machine learning techniques to neuroimaging-based diagnosis. 4 key findings →
Evidence: low
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ATTRACTnet was developed in an internal test set of 799 patients (mean [SD] age, 75.1 [11.1] years; 516 [64.7%] male and 283 [35.3%] female) using 5-fold cross-validation with an additional external test set of 422 patients. 5 key findings →
Design: open-label trial Participants: 799 Evidence: high
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Early detection of Alzheimer's disease (AD) is critical yet challenging, particularly in younger individuals. 1 key finding →
Design: longitudinal study Participants: 120 Evidence: moderate
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Alzheimer's disease (AD) remains a formidable challenge in modern healthcare, necessitating innovative approaches for its early detection and intervention. 2 key findings →
Design: longitudinal study Evidence: moderate
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Model scores two standard deviations below the mean had 4 months increased survival (two standard deviations below had 3 months decreased survival). 5 key findings →
Participants: 802 Evidence: low
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New personalized endpoints that are clinically meaningful to individuals and their families should be developed. 1 key finding →
Evidence: low
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Across both cohorts, individuals with lower SES generally exhibited higher BERs (worse performance) than high SES groups, confirming the presence of bias. 5 key findings →
Participants: 3,041 Evidence: moderate
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Machine learning has become ubiquitous and a key technology on mining electronic health records (EHRs) for facilitating clinical research and practice. 2 key findings →
Evidence: low
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Accurate estimation of Alzheimer's disease (AD) severity from structural magnetic resonance imaging (MRI) remains difficult, as disease-associated anatomical alterations are often subtle and publicly available datasets are typically too small to support robust deep learning model training. 1 key finding →
Participants: 214 Evidence: moderate
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Caregivers in the control conditions reported significantly worsening sleep efficiency whereas in comparison, those in the active conditions reported improving sleep efficiency. 4 key findings →
Evidence: low
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Inexpensive, non-invasive tools for assessing Alzheimer-type pathophysiologies are needed. 1 key finding →
Evidence: low
Full summary, score breakdown and related studies → Original source Free full text
The study of Alzheimer's disease (AD)-associated mutations has implicated dysregulation of amyloid precursor protein (APP) proteolysis in the disease. 3 key findings →
Evidence: low
Full summary, score breakdown and related studies → Original source Free full text
The smart device system to monitor tremors and to recognize its phenotypes for better outcomes of deep brain stimulation, applications evaluating fine motor functions, AI integrated electroencephalogram learning to diagnose epilepsy and psychological non-epileptic seizure, predict outcome of seizure surgeries, recognize patterns of autonomic instability to prevent sudden unexpected death in epilepsy (SUDEP), identify the pattern of complex algorithm in neuroimaging classifying cognitive impairment, differentiating and classifying concussion phenotypes, smartwatches monitoring atrial fibrillation to prevent strokes, and prediction of prognosis in dementia are unique examples of experimental utilizations of AI in the field of neurology. 1 key finding →
Evidence: low
Full summary, score breakdown and related studies → Original source
IntroductionFrontotemporal lobar degeneration (FTLD) is a devastating disease that commonly results in early onset dementia, yet its rarity and heterogeneity limit large-scale research and clinical trials. 2 key findings →
Design: longitudinal study Participants: 567 Evidence: moderate
Full summary, score breakdown and related studies → Original source Free full text
Free full text links and the Full text may need a subscription label come from Unpaywall, Europe PMC and OpenAlex records of free copies, checked 10 Oct 2026. A study with neither is unknown, not paywalled. Show only studies that are free to read
This is alzhub's own score. It is not an official metric, not an impact factor, and no journal, publisher or ranking body endorses it. We built it so that a reader who is not a scientist can tell a landmark trial from a conference abstract without having to already know the difference. Every point comes from one of 20 components, all of them printed on the study itself, so you can disagree with our arithmetic rather than having to trust the total.
Journals are placed in ordered tiers, never given a numeric impact factor — we could not verify current impact factors from a source we control, and an invented one would be worse than none. Tier 1 is the top-tier general journals (NEJM, The Lancet, JAMA, Nature, Science, Nature Medicine); tier 2 is the leading specialty journals; tier 3 is every other peer-reviewed journal. A separate part scores how much the journal's own papers are cited, which tells the tier-3 journals apart.
Two rules we don't bend. First, we never fill in a missing value: if a record does not state a sample size or a design, that component scores zero, and if we simply have no data for a part, such as a citation count, that part is left out and the study is scored on the parts we could check. The study card says which. Second, anything that is not a peer-reviewed article is capped at 16/100 and carries a warning chip — so a conference abstract or a company press release can never outrank a peer-reviewed paper, no matter how good it looks.
We rank a rigorous null result — a good trial showing that something does not work — above an early positive finding. Someone deciding whether to buy a supplement is served by that, and the rigour is already counted by the design and journal components.
The score is computed from the published data alone and is reproducible: the same file always yields the same numbers.
See the whole rubric drawn out → — four charts covering all 27,380 studies: how the scores are spread and where the cap bites, which of the 20 components the library is strong and weak on, which subjects rest on stronger evidence, and what kinds of study each subject is actually built from.
This is a large sample of the Alzheimer's literature, not an exhaustive index of it. It holds 27,380 records, built mostly from automated pulls of Europe PMC (60.6% of the library) and OpenAlex (18.6%), plus smaller pulls from PubMed, Crossref and medRxiv. If a study, a drug, or a finding isn't here, that is not evidence it doesn't exist in the literature — it may simply not have been pulled in yet.
1,032 records (3.8% of the library) are not peer-reviewed — 962 of those are preprints posted before any journal review, and the rest are conference abstracts, regulatory notices and reports. Every study card on this page states its status plainly, and the score above is capped for anything that isn't a peer-reviewed article.
See the full, machine-readable breakdown of this library's coverage →