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Linguistic markers predict onset of Alzheimer's disease
Eyigoz E, Mathur S, Santamaria M, Cecchi G, Naylor M.. EClinicalMedicine, 2020-10-22
Topic: Amyloid, tau and blood-test diagnosis
Background The aim of this study is to use classification methods to predict future onset of Alzheimer's disease in cognitively normal subjects through automated linguistic analysis. Methods To study linguistic performance as an early biomarker of AD, we performed predictive modeling of future diagnosis of AD from a cognitively normal baseline of Framingham Heart Study participants. The linguistic variables were derived from written responses to the cookie-theft picture-description task.
Cite this study APA, Vancouver, BibTeX · download .bib or .ris
APA 7th
Eyigoz, E., Mathur, S., Santamaria, M., Cecchi, G., & Naylor, M. (2020). Linguistic markers predict onset of Alzheimer's disease. EClinicalMedicine. https://doi.org/10.1016/j.eclinm.2020.100583
Vancouver
Eyigoz E, Mathur S, Santamaria M, Cecchi G, Naylor M. Linguistic markers predict onset of Alzheimer's disease. EClinicalMedicine. 2020. doi:10.1016/j.eclinm.2020.100583. PMID: 33294808.
BibTeX
@article{eyigoz2020-33294808,
author = {Eyigoz, E. and Mathur, S. and Santamaria, M. and Cecchi, G. and Naylor, M.},
title = {{Linguistic markers predict onset of Alzheimer's disease}},
journal = {EClinicalMedicine},
year = {2020},
doi = {10.1016/j.eclinm.2020.100583},
pmid = {33294808},
url = {https://doi.org/10.1016/j.eclinm.2020.100583}
}
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.
Key findings
- Methods To study linguistic performance as an early biomarker of AD, we performed predictive modeling of future diagnosis of AD from a cognitively normal baseline of Framingham Heart Study participants.
- The linguistic variables were derived from written responses to the cookie-theft picture-description task.
- We compared the predictive performance of linguistic variables with clinical and neuropsychological variables.
- The study included 703 samples from 270 participants out of which a dataset consisting of a single sample from 80 participants was held out for testing.
Why it matters
This study used automated linguistic analysis of written responses to predict future onset of Alzheimer's disease in cognitively normal participants. It is a predictive modeling study of a possible early marker and does not test any treatment, cure, or prevention.
Study details
- Participants703
- Evidence strengthLow
How this study scored 52/100
Published in a peer-reviewed journal. Scored zero for study design, effect reported and randomized / blinded because the record does not state it.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (EClinicalMedicine) |
| Journal's citation record | 2 / 4 | EClinicalMedicine: journal h-index 150 (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 | 2 / 5 | 2020 — within 10 year(s) of the newest study here |
| Sample size | 2 / 4 | 703 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) |
| Randomized / blinded | 0 / 2 | No randomization or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | 5 / 7 | Cited 206 times (OpenAlex, 2026-10-10) |
| Citations per year | 4 / 5 | 25.8 citations a year over 8 year(s) since 2020 |
| Impact for its field and year | 4 / 6 | Cited 15.7× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 2 / 6 | Cited by 9 other studies in this library |
| Still being cited | 3 / 4 | Cited 108 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 1 / 4 | Last-listed author Melissa Naylor: h-index 12 (OpenAlex, whole career) |
| Institution's track record | 1 / 3 | IBM Research - Thomas J. Watson Research Center: institution h-index 883 (OpenAlex) |
| Multicenter collaboration | 1 / 3 | OpenAlex lists 2 institution(s) in 1 country |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 33294808) |
| Free to read | 2 / 2 | Free to read |
| Total | 52 / 100 | — |
What each of these 20 components measures, and what this score is not →
Read the original
- DOI: 10.1016/j.eclinm.2020.100583
- PubMed: 33294808
- Original source →
- Free full text: EClinicalMedicine →
Full text: free to read — OpenAlex lists this copy as free to read: the link above (checked 6 Feb 1020).
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