Evidence map›Paper›PMID 36721487›Full record

ArticleJournal of Alzheimer's disease reports2022

Application of Structural Retinal Biomarkers to Detect Cognitive Impairment in a Primary Care Setting.

Sima Mozdbar, Melissa Petersen, Fan Zhang, Leigh Johnson, Alex Tolman, Ramyashree Nyalakonda, Alejandra Gutierrez, Sid O'Bryant

Abstract read
In one paragraph

Article in Journal of Alzheimer's disease reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Sima MozdbarUniversity of North Texas Health Science Center, Department of Pharmacology & Neuroscience, Fort Worth, TX, USA.
Melissa PetersenUniversity of North Texas Health Science Center, Texas College of Osteopathic Medicine, Fort Worth, TX, USA.
Fan ZhangUniversity of North Texas Health Science Center, Texas College of Osteopathic Medicine, Fort Worth, TX, USA.
Leigh JohnsonUniversity of North Texas Health Science Center, Department of Pharmacology & Neuroscience, Fort Worth, TX, USA.
Alex TolmanAnne Burnett Marion School of Medicine at Texas Christian University, Fort Worth, TX, USA.
Ramyashree NyalakondaUniversity of North Texas Health Science Center, Texas College of Osteopathic Medicine, Fort Worth, TX, USA.
Alejandra GutierrezAnne Burnett Marion School of Medicine at Texas Christian University, Fort Worth, TX, USA.
Sid O'BryantUniversity of North Texas Health Science Center, Texas College of Osteopathic Medicine, Fort Worth, TX, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Despite the diagnostic accuracy of advanced neurodiagnostic procedures, the detection of Alzheimer's disease (AD) remains poor in primary care. There is an urgent need for screening tools to aid in the detection of early AD. Objective: This study examines the predictive ability of structural retinal biomarkers in detecting cognitive impairment in a primary care setting. Methods: Participants were recruited from Alzheimer's Disease in Primary Care (ADPC) study. As part of the ADPC Retinal Biomarker Study (ADPC RBS), visual acuity, an ocular history questionnaire, eye pressure, optical coherence tomography (OCT) imaging, and fundus imaging was performed. Results: Data were examined on Conclusion: The current data provides strong support for continued investigation into structural retinal biomarkers, particularly the retinal nerve fiber layer, as screening tools for AD.

Indexed as

Alzheimer’s diseasecognitive impairmentoptical coherence tomographyretinal biomarkers

Identifiers

PMID36721487
PMCPMC9837730

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.