27,380 Alzheimer's and dementia studies in plain English, each scored out of 100.
Selection criteria We included randomised controlled trials (RCTs) and quasi-RCTs in which cognitive training via interactive computerised technology was compared with an active or inactive control intervention. 5 key findings →
Participants: 660 Evidence: high
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
Due to the growing number of older adults with cognitive impairment, it is essential to delay the onset and progression of cognitive decline and promote a healthy lifestyle. 1 key finding →
Design: systematic review Evidence: high
Full summary, score breakdown and related studies → Original source Free full text
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
Full summary, score breakdown and related studies → Original source Free full text
(n = 16,683) had an average age of 69.07 ± 7.34, 72.04% were female, and had an average of 16.66 ± 2.26 years of education. 5 key findings →
Design: longitudinal study Participants: 16,683 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
Full summary, score breakdown and related studies → Original source Free full text
Alzheimer's disease (AD) is a progressive and irreversible brain degenerative disorder. 2 key findings →
Evidence: low
Full summary, score breakdown and related studies → Original source Free full text
The INNO-BIA AlzBio3 selectively and specifically measured Abeta((1-42)), T-TAU, and P-TAU((181P)) in the CSF. 1 key finding →
Evidence: low
Full summary, score breakdown and related studies → Original source Full text may need a subscription
This study aims to identify depressive risks in elderly individuals with subjective cognitive decline (SCD) and develop a predictive model using machine learning algorithms to enable timely interventions. 3 key findings →
Design: longitudinal study Evidence: moderate
Full summary, score breakdown and related studies → Original source Free full text
Mild cognitive impairment (MCI) is a significant and increasingly recognized problem in individuals with type 2 diabetes mellitus (T2DM). 2 key findings →
Design: prospective cohort Participants: 100 Evidence: moderate
Full summary, score breakdown and related studies → Original source Free full text
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
Full summary, score breakdown and related studies → Original source Free full text
Alzheimer’s disease (AD), a leading neurodegenerative disorder, progresses from an intermediary stage known as Mild Cognitive Impairment (MCI), characterized by measurable cognitive decline with retained functional independence. 1 key finding →
Design: longitudinal study Evidence: moderate
Full summary, score breakdown and related studies → Original source Free full text
Mild cognitive impairment (MCI) is a clinical condition characterized by a decline in cognitive ability and progression of cognitive impairment. 3 key findings →
Participants: 239 Evidence: moderate
Full summary, score breakdown and related studies → Original source Free full text
The results indicated that residence location, alcohol consumption, life satisfaction, depressive symptoms, and education level are key factors influencing the risk of MCI among older adults with functional disability. 3 key findings →
Design: longitudinal study Evidence: moderate
Full summary, score breakdown and related studies → Original source Free full text
EEG/ERP analysis suggested differences in frontal alpha asymmetry, power ratio, and ERP components between the MCI and CN groups. 5 key findings →
Participants: 70 Evidence: low
Full summary, score breakdown and related studies → Original source Free full text
Third, we benchmarked the results of our virtual reality episodic memory test against a classical memory test and a subjective daily memory complaint scale. 3 key findings →
Evidence: low
Full summary, score breakdown and related studies → Original source Full text may need a subscription
Mild Cognitive Impairment (MCI) is a transitional stage between normal age-related cognitive decline and the more severe cognitive loss seen in dementia. 1 key finding →
Evidence: low
Full summary, score breakdown and related studies → Original source Free full text
Overall, both machine learning techniques had an equivalent performance. 4 key findings →
Evidence: low
Full summary, score breakdown and related studies → Original source Free full text
Accurate prediction of the progression from mild cognitive impairment (MCI) to Alzheimer's disease (AD) is essential for early intervention and effective disease management. 1 key finding →
Evidence: low
Full summary, score breakdown and related studies → Original source Full text may need a subscription
The feasibility criteria of >80% was met. 5 key findings →
Evidence: high
Full summary, score breakdown and related studies → Original source Free full text
Parkinson’s disease (PD) exhibits considerable variability in symptom onset, severity, and clinical trajectories, making prediction of individual disease progression a complex challenge. 1 key finding →
Evidence: low
Full summary, score breakdown and related studies → Original source
Performance variability precedes measurable linear decline across PD-risk stratified groups. 4 key findings →
Design: longitudinal study Evidence: moderate
Full summary, score breakdown and related studies → Original source
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 →