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40/100

✓ Peer-reviewed alzhub usefulness score 40 out of 100. Digital health tools

Topic: Risk, prevention and care in population studies

Using raw data from the CFD and the GLMnet algorithm, we could accurately differentiate between DEM vs. HC with accuracies ranging up to 94.0%. Similarly, we could classify DEM and DEP with accuracies up to 80.8% using the Naïve Bayes algorithm and raw scores. Measures for verbal memory, word fluency and processing speed showed the highest feature importance within these classifications, highlighting their importance for differential diagnosis. We provide preliminary evidence that ML algorithms in combination with the CFD can aid clinicians in the differential diagnosis of HC and DEM, as well as DEM and DEP by providing a decision-making aid.

Key findings

Using raw data from the CFD and the GLMnet algorithm, we could accurately differentiate between DEM vs.
Similarly, we could classify DEM and DEP with accuracies up to 80.8% using the Naïve Bayes algorithm and raw scores.
Measures for verbal memory, word fluency and processing speed showed the highest feature importance within these classifications, highlighting their importance for differential diagnosis.
We provide preliminary evidence that ML algorithms in combination with the CFD can aid clinicians in the differential diagnosis of HC and DEM, as well as DEM and DEP by providing a decision-making aid.

Why it matters

Machine learning combined with the Cognitive Functions Dementia test set differentiated dementia from healthy controls with accuracies up to 94.0%, and dementia from depression with accuracies up to 80.8%. The authors describe this as preliminary evidence that these algorithms could aid clinicians in differential diagnosis, not as a treatment or cure.

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Study details

Participants407
Effect size94.0%; 80.8%
Evidence strengthLow

How this study scored 40/100

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

Component	Points	Why
Journal tier	7 / 10	Tier 3 — peer-reviewed journal (The Clinical Neuropsychologist)
Journal's citation record	1 / 4	The Clinical Neuropsychologist: journal h-index 128 (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	407 participants
Effect reported	2 / 3	Effect reported as a number, without an interval or test
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 1 times (OpenAlex, 2026-10-10)
Citations per year	0 / 5	0.3 citations a year over 3 year(s) since 2025
Impact for its field and year	1 / 6	Cited 0.6× 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 1 times in 2024–2026 (2026 still in progress; OpenAlex)
Senior author's track record	2 / 4	Last-listed author Thomas Jähn: h-index 27 (OpenAlex, whole career)
Institution's track record	1 / 3	Ludwig-Maximilians-Universität München: institution h-index 1,224 (OpenAlex)
Multi-centre collaboration	1 / 3	OpenAlex lists 2 institution(s) in 1 country
Indexed in PubMed	2 / 2	Indexed in PubMed (PMID 40459338)
Free to read	2 / 2	Free to read
Total	40 / 100	—

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

Read the original

Full text: free to read — OpenAlex lists this copy as free to read: the link above (checked 6 Feb 1020).

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

Built from the record alzhub holds for this paper, not looked up. Volume, issue and page numbers are not held in the record. Check the citation against the original before you use it.

APA 7th

Dominke, C., Schenk, T., & Jähn, T. (2025). Differentiating patients with dementia from patients with depression and healthy controls using the Cognitive Functions Dementia (CFD) test set and machine learning. The Clinical Neuropsychologist. https://doi.org/10.1080/13854046.2025.2513446

Vancouver

Dominke C, Schenk T, Jähn T. Differentiating patients with dementia from patients with depression and healthy controls using the Cognitive Functions Dementia (CFD) test set and machine learning. The Clinical Neuropsychologist. 2025. doi:10.1080/13854046.2025.2513446. PMID: 40459338.

BibTeX

@article{dominke2025-40459338,
  author  = {Dominke, Clara and Schenk, Thomas and Jähn, Thomas},
  title   = {{Differentiating patients with dementia from patients with depression and healthy controls using the Cognitive Functions Dementia (CFD) test set and machine learning}},
  journal = {The Clinical Neuropsychologist},
  year    = {2025},
  doi     = {10.1080/13854046.2025.2513446},
  pmid    = {40459338},
  url     = {https://doi.org/10.1080/13854046.2025.2513446}
}

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