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42/100

✓ Peer-reviewed alzhub usefulness score 42 out of 100. Cognitive tests and assessment

Topic: Amyloid, tau and blood-test diagnosis

The DET framework has achieved superior performance on the classification task across all the evaluation metrics compared to the traditional machine learning based methods, as demonstrated in Table 1. Figure 2 shows that the CN, AD patients, LBD patients are projected to the DET based on the learned representations in the embedding space. The DET framework effectively models the continuous relationships among CN, AD, and LBD subjects and outperforms the traditional models in the classification task. By leveraging cortical features of the cortical hubs and the MMSE-based constraints, it provides valuable insights into disease progression.

Cite this study APA, Vancouver, BibTeX · download .bib or .ris

APA 7th

Chen, T., Chen, M., Zhuang, Y., Zhang, J., Zhang, L., Liu, T., et al. (2025). Representing the Cognitive Impairment Continuum of Alzheimer's Disease and Lewy Body Dementia with a Novel Finer‐Scale Cortical Representation via Disease Embedding Tree. Alzheimer s & Dementia. https://doi.org/10.1002/alz70856_102179

Vancouver

Chen T, Chen M, Zhuang Y, Zhang J, Zhang L, Liu T, et al. Representing the Cognitive Impairment Continuum of Alzheimer's Disease and Lewy Body Dementia with a Novel Finer‐Scale Cortical Representation via Disease Embedding Tree. Alzheimer s & Dementia. 2025. doi:10.1002/alz70856_102179. PMID: 41442571.

BibTeX

@article{chen2025-41442571,
  author  = {Chen, Tong and Chen, Minheng and Zhuang, Yan and Zhang, Jing and Zhang, Lu and Liu, Tianming and Blamire, Andrew Matthew and O'Brien, John T and others},
  title   = {{Representing the Cognitive Impairment Continuum of Alzheimer's Disease and Lewy Body Dementia with a Novel Finer‐Scale Cortical Representation via Disease Embedding Tree}},
  journal = {Alzheimer s \& Dementia},
  year    = {2025},
  doi     = {10.1002/alz70856_102179},
  pmid    = {41442571},
  url     = {https://doi.org/10.1002/alz70856_102179}
}

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Key findings

The DET framework has achieved superior performance on the classification task across all the evaluation metrics compared to the traditional machine learning based methods, as demonstrated in Table 1.
Figure 2 shows that the CN, AD patients, LBD patients are projected to the DET based on the learned representations in the embedding space.
The DET framework effectively models the continuous relationships among CN, AD, and LBD subjects and outperforms the traditional models in the classification task.
By leveraging cortical features of the cortical hubs and the MMSE-based constraints, it provides valuable insights into disease progression.

Why it matters

This study describes a new computational framework that classified people with Alzheimer's disease, Lewy body dementia and cognitively normal controls better than traditional machine learning methods. It is a technical modeling study and does not test any treatment or prevention.

Study details

Participants106
Evidence strengthLow

How this study scored 42/100

Published in a peer-reviewed journal. Scored zero for study design, effect reported and randomised / blinded because the record does not state it.

Component	Points	Why
Journal tier	7 / 10	Tier 3 — peer-reviewed journal (Alzheimer s & Dementia)
Journal's citation record	2 / 4	Alzheimer s & Dementia: journal h-index 228 (OpenAlex, 2026-10-29)
Study design	0 / 12	No study design recorded — scores zero, not guessed (not stated in the record — scored 0)
Strength of evidence	1 / 5	Rated “low” in our summary
Peer-review validation	10 / 10	Peer-reviewed. Published as an article in a named peer-reviewed journal.
How recent	4 / 5	2025 — within 3 year(s) of the newest study here
Sample size	2 / 4	106 participants
Effect reported	0 / 3	No effect size reported — scores zero (not stated in the record — scored 0)
Studied in people	3 / 3	Studied in people (participant count stated)
Randomised / blinded	0 / 2	No randomisation or blinding stated — scores zero (not stated in the record — scored 0)
Times cited	1 / 7	Cited 2 times (OpenAlex, 2026-10-10)
Citations per year	0 / 5	0.7 citations a year over 3 year(s) since 2025
Impact for its field and year	2 / 6	Cited 1.3× the average for papers of its field and year (OpenAlex FWCI)
Cited by studies here	0 / 6	No other study in this library cites it
Still being cited	0 / 4	Cited 2 times in 2024–2026 (2026 still in progress; OpenAlex)
Senior author's track record	2 / 4	Last-listed author Dajiang Zhu: h-index 38 (OpenAlex, whole career)
Institution's track record	2 / 3	University of Cambridge: institution h-index 1,973 (OpenAlex)
Multi-centre collaboration	2 / 3	OpenAlex lists 6 institution(s) in 2 countries
Indexed in PubMed	2 / 2	Indexed in PubMed (PMID 41442571)
Free to read	2 / 2	Free to read
Total	42 / 100	—

What each of these 20 components measures, and what this score is not →

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