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49/100

✓ Peer-reviewed alzhub usefulness score 49 out of 100. Long-term cohort studies

Topic: Amyloid, tau and blood-test diagnosis

Alzheimer's disease (AD) is characterised by a dynamic process of neurocognitive changes from normal cognition to mild cognitive impairment (MCI) and progression to dementia. However, not all individuals with MCI develop dementia. Predicting whether individuals with MCI will decline (i.e. progressive MCI) or remain stable (i.e. stable MCI) is impeded by patient heterogeneity due to comorbidities that may lead to MCI diagnosis without progression to AD.

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

APA 7th

Giorgio, J., Landau, S. M., Jagust, W. J., Tino, P., Kourtzi, Z., & Alzheimer's Disease Neuroimaging Initiative. (2020). Modelling prognostic trajectories of cognitive decline due to Alzheimer's disease. NeuroImage. Clinical. https://doi.org/10.1016/j.nicl.2020.102199

Vancouver

Giorgio J, Landau SM, Jagust WJ, Tino P, Kourtzi Z, Alzheimer's Disease Neuroimaging Initiative. Modelling prognostic trajectories of cognitive decline due to Alzheimer's disease. NeuroImage. Clinical. 2020. doi:10.1016/j.nicl.2020.102199. PMID: 32106025.

BibTeX

@article{giorgio2020-32106025,
  author  = {Giorgio, J. and Landau, S. M. and Jagust, W. J. and Tino, P. and Kourtzi, Z. and {Alzheimer's Disease Neuroimaging Initiative}},
  title   = {{Modelling prognostic trajectories of cognitive decline due to Alzheimer's disease}},
  journal = {NeuroImage. Clinical},
  year    = {2020},
  doi     = {10.1016/j.nicl.2020.102199},
  pmid    = {32106025},
  url     = {https://doi.org/10.1016/j.nicl.2020.102199}
}

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Key findings

However, not all individuals with MCI develop dementia.
Predicting whether individuals with MCI will decline (i.e. progressive MCI) or remain stable (i.e. stable MCI) is impeded by patient heterogeneity due to comorbidities that may lead to MCI diagnosis without progression to AD.
Despite the importance of early diagnosis of AD for prognosis and personalised interventions, we still lack robust tools for predicting individual progression to dementia.
Here, we propose a novel trajectory modelling approach based on metric learning (Generalised Metric Learning Vector Quantization) that mines multimodal data from MCI patients in the Alzheimer's disease Neuroimaging Initiative (ADNI) cohort to derive individualised prognostic scores of cognitive decline due to AD.

Why it matters

This paper proposes a trajectory modelling approach to predict which people with mild cognitive impairment will decline to dementia and which will remain stable. It is a prediction modelling study, so it does not treat, cure, or prevent Alzheimer's disease.

Study details

Effect size81.4%; 81.9%
Evidence strengthModerate

How this study scored 49/100

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

Component	Points	Why
Journal tier	7 / 10	Tier 3 — peer-reviewed journal (NeuroImage. Clinical)
Journal's citation record	1 / 4	NeuroImage Clinical: journal h-index 133 (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	3 / 5	Rated “moderate” in our summary
Peer-review validation	10 / 10	Peer-reviewed. Published as an article in a named peer-reviewed journal.
How recent	2 / 5	2020 — within 10 year(s) of the newest study here
Sample size	0 / 4	Participant count not stated — scores zero (not stated in the record — scored 0)
Effect reported	2 / 3	Effect reported as a number, without an interval or test
Studied in people	0 / 3	Not stated whether this was studied in people — scores zero (not stated in the record — scored 0)
Randomised / blinded	0 / 2	No randomisation or blinding stated — scores zero (not stated in the record — scored 0)
Times cited	4 / 7	Cited 79 times (OpenAlex, 2026-10-10)
Citations per year	3 / 5	9.9 citations a year over 8 year(s) since 2020
Impact for its field and year	4 / 6	Cited 8.6× the average for papers of its field and year (OpenAlex FWCI)
Cited by studies here	2 / 6	Cited by 4 other studies in this library
Still being cited	2 / 4	Cited 25 times in 2024–2026 (2026 still in progress; OpenAlex)
Senior author's track record	2 / 4	Last-listed author Zoe Kourtzi: h-index 45 (OpenAlex, whole career)
Institution's track record	2 / 3	University of California, Berkeley: institution h-index 2,012 (OpenAlex)
Multi-centre collaboration	1 / 3	OpenAlex lists 3 institution(s) in 2 countries
Indexed in PubMed	2 / 2	Indexed in PubMed (PMID 32106025)
Free to read	2 / 2	Free to read
Total	49 / 100	—

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

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