27,380 Alzheimer's and dementia studies in plain English, each scored out of 100.
Across the cohorts, each 10-year increase in BAI was associated with a 39% higher risk of incident dementia (hazard ratio [HR], 1.39 [95% CI, 1.21-1.59]; P Conclusions and relevance In this IPD meta-analysis, a higher sleep EEG-based BAI was associated with a higher risk of incident dementia. 3 key findings →
Design: meta-analysis Participants: 7,105 Evidence: moderate
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The literature has several methodological limitations including lack of sufficient algorithm development descriptions and standard definitions. 1 key finding →
Participants: 255 Evidence: moderate
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Targeted clinical application of specific technologies is poised to revolutionize precision care in dementia as these technologies may be used both by patients and caregivers, and at a systems level to provide safe and effective care. 2 key findings →
Design: systematic review Participants: 1,337 Evidence: high
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Traditional neuroimage analysis pipelines involve computationally intensive, time-consuming optimization steps, and thus, do not scale well to large cohort studies with thousands or tens of thousands of individuals. 5 key findings →
Design: cohort study Evidence: moderate
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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
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We developed machine learning prediction models for post-operative delirium that performed better than chance and are comparable with traditional stepwise logistic regression. 1 key finding →
Design: cohort study Participants: 134 Evidence: moderate
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Online education involving a celebrity may be an effective strategy for educating college students about AD risk reduction strategies. 1 key finding →
Design: randomized controlled trial Participants: 721 Evidence: high
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The testing conditions and the accessibility of telemedicine yield inconclusive results as to whether telemedicine can improve the management of dementia in geriatric individuals. 4 key findings →
Design: systematic review Evidence: high
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The most novel characteristics of our machine learning model compared to previous ones are the following: 1) our deep learning model is multi-tasking, in the sense that it jointly learns to simultaneously predict both MCI to AD conversion as well as AD vs. healthy controls classification, which facilitates relevant feature extraction for AD prognostication; 2) the neural network classifier employs fewer parameters than other deep learning architectures which significantly limits data-overfitting (we use ∼550,000 network parameters, which is orders of magnitude lower than other network designs); 3) both structural MRI images and their warp field characteristics, which quantify local volumetric changes in relation to the MRI template, were used as separate input streams to extract as much information as possible from the MRI data. 1 key finding →
Participants: 785 Evidence: low
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Caregiver stress is associated with negative outcomes for both caregivers and care recipients. 5 key findings →
Design: randomized controlled trial Participants: 14 Evidence: high
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DCR outperforms the MMSE in detecting and classifying cognitive impairment-in a fraction of the time-while being not influenced by a patient's ethnicity. 5 key findings →
Participants: 360 Evidence: low
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Between 2011 and 2014, seniors experiencing health decline used various digital health technologies at low absolute rates (range: 1%-20%). 2 key findings →
Design: longitudinal study Participants: 4,037 Evidence: moderate
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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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Converging evidence suggests that the accumulation of cerebral amyloid beta-protein (Abeta) in Alzheimer's disease (AD) reflects an imbalance between the production and degradation of this self-aggregating peptide. 3 key findings →
Evidence: low
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The prevalence of dementia in Latin America and the Caribbean is growing rapidly, increasing the burden placed on caregivers. 5 key findings →
Evidence: low
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The clinical prediction model can play a beneficial role in decision-making for preventative measures for patients at risk of developing a delirium. 3 key findings →
Participants: 207 Evidence: low
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The study of Alzheimer's disease (AD), the most common cause of dementia, faces challenges in terms of understanding the cause, monitoring the pathogenesis, and developing early diagnoses and effective treatments. 5 key findings →
Evidence: low
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Our results demonstrate that the repeatability of speech features extracted using open-source tool kits is low. 3 key findings →
Design: longitudinal study Evidence: moderate
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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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White matter hyperintensities (WMH) are neuroimaging markers linked to an elevated risk of cognitive decline. 2 key findings →
Participants: 60 Evidence: low
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Aβ is the main component of amyloid deposits in Alzheimer disease (AD) and its aggregation into oligomers, protofibrils and fibrils is considered a seminal event in the pathogenesis of AD. 2 key findings →
Evidence: low
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The machine learning model predicted MMSE scores with a root-mean-squared error (RMSE) of 2.43 in the BH test set. 4 key findings →
Participants: 945 Evidence: moderate
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Digital phenotyping based on physiological parameters and sensing technology may increase pain assessment objectivity in older adults with dementia. 2 key findings →
Evidence: low
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Familial Alzheimer's disease (FAD) is caused by dominant missense mutations in amyloid precursor protein (APP) and presenilin-1 (PSEN1), the catalytic component of γ-secretase that generates amyloid β-peptides (Aβ) from the APP C-terminal fragment C99. 3 key findings →
Evidence: low
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RoCA classifies patients similarly to gold standard paper-based tests, with a receiver operating characteristic area under the curve of 0.81 (95% CI 0.67-0.91; P<.001). 5 key findings →
Design: open-label trial Participants: 35 Evidence: low
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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 →