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43/100

✓ Peer-reviewed alzhub usefulness score 43 out of 100. Cognitive tests and assessment

Topic: Amyloid, tau and blood-test diagnosis

Results The machine learning model predicted MMSE scores with a root-mean-squared error (RMSE) of 2.43 in the BH test set. External validation in the Apheleia cohort demonstrated robust generalizability (RMSE = 2.62). Exploratory demographic analyses on prediction errors, including Age, Sex, Race, and Ethnicity, yielded significant differences only for Sex and Age in Apheleia, with signed errors becoming progressively more negative (i.e., increasing under-prediction) at older ages for the latter. This scarcity of statistical differences across cohorts suggested that our predictions were fair.

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

APA 7th

Jannati, A., Toro‐Serey, C., Ciesla, M., Chen, E., Showalter, J., Bates, D., et al. (2026). Streamlining eligibility assessment for Alzheimer's disease-modifying therapies: Prediction of MMSE scores using the digital clock and recall. Frontiers in Digital Health. https://doi.org/10.3389/fdgth.2026.1799372

Vancouver

Jannati A, Toro‐Serey C, Ciesla M, Chen E, Showalter J, Bates D, et al. Streamlining eligibility assessment for Alzheimer's disease-modifying therapies: Prediction of MMSE scores using the digital clock and recall. Frontiers in Digital Health. 2026. doi:10.3389/fdgth.2026.1799372. PMID: 42495050.

BibTeX

@article{jannati2026-42495050,
  author  = {Jannati, Ali and Toro‐Serey, Claudio and Ciesla, Marissa and Chen, Emma and Showalter, John and Bates, David and Pascual‐Leone, Álvaro and Tobyne, Sean M.},
  title   = {{Streamlining eligibility assessment for Alzheimer's disease-modifying therapies: Prediction of MMSE scores using the digital clock and recall}},
  journal = {Frontiers in Digital Health},
  year    = {2026},
  doi     = {10.3389/fdgth.2026.1799372},
  pmid    = {42495050},
  url     = {https://doi.org/10.3389/fdgth.2026.1799372}
}

Built from the record alzhub holds for this paper, not looked up. Volume, issue and page numbers are not held in the record. The record names 8 authors; APA and Vancouver show the first six, BibTeX and the downloads carry all of them. Check the citation against the original before you use it.

Key findings

Results The machine learning model predicted MMSE scores with a root-mean-squared error (RMSE) of 2.43 in the BH test set.
External validation in the Apheleia cohort demonstrated robust generalizability (RMSE = 2.62).
Exploratory demographic analyses on prediction errors, including Age, Sex, Race, and Ethnicity, yielded significant differences only for Sex and Age in Apheleia, with signed errors becoming progressively more negative (i.e., increasing under-prediction) at older ages for the latter.
This scarcity of statistical differences across cohorts suggested that our predictions were fair.

Why it matters

A machine learning model using a digital clock and recall test predicted MMSE scores with reasonable accuracy, which could help streamline eligibility assessment for Alzheimer's disease-modifying therapies. This is a tool for assessment, not a treatment or prevention for the disease itself.

Study details

Participants945
Evidence strengthModerate

How this study scored 43/100

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 (Frontiers in Digital Health)
Journal's citation record	0 / 4	Frontiers in Digital Health: journal h-index 66 (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	5 / 5	2026 — within 1 year(s) of the newest study here
Sample size	2 / 4	945 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 1 times (OpenAlex, 2026-10-10)
Citations per year	0 / 5	0.5 citations a year over 2 year(s) since 2026
Impact for its field and year	3 / 6	Cited 4.7× 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	1 / 4	Last-listed author Sean M. Tobyne: h-index 17 (OpenAlex, whole career)
Institution's track record	3 / 3	Harvard University: institution h-index 3,235 (OpenAlex)
Multi-centre collaboration	1 / 3	OpenAlex lists 2 institution(s) in 1 country
Indexed in PubMed	2 / 2	Indexed in PubMed (PMID 42495050)
Free to read	2 / 2	Free to read
Total	43 / 100	—

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

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