ArticleAlzheimer's & dementia (Amsterdam, Netherlands)2020
Predicting amyloid status using self-report information from an online research and recruitment registry: The Brain Health Registry.
Article in Alzheimer's & dementia (Amsterdam, Netherlands), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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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.
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.
Who cites it
6 citing papers in PubMed, 9 citations in OpenAlex.
- Examining Demographic Factors, Psychosocial Wellbeing and Cardiovascular Health in Subjective Cognitive Decline in the Brain Health Registry Cohort.The journal of prevention of Alzheimer's disease · 2024Article
- Brain health registry updates: An online longitudinal neuroscience platform.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2023Article
- Algorithmic Fairness of Machine Learning Models for Alzheimer Disease Progression.JAMA network open · 2023Article
- Potential Applications of Artificial Intelligence in Clinical Trials for Alzheimer's Disease.Life (Basel, Switzerland) · 2022Review
- Machine learning approaches to predicting amyloid status using data from an online research and recruitment registry: The Brain Health Registry.Alzheimer's & dementia (Amsterdam, Netherlands) · 2021Article
- Predicting amyloid status using self-report information from an online research and recruitment registry: The Brain Health Registry.Alzheimer's & dementia (Amsterdam, Netherlands) · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors at 4 institutions in 1 country.
Funding
Abstract
introductionThis study aimed to predict brain amyloid beta (Aβ) status in older adults using collected information from an online registry focused on cognitive aging.
methodsAβ positron emission tomography (PET) was obtained from multiple in-clinic studies. Using logistic regression, we predicted Aβ using self-report variables collected in the Brain Health Registry in 634 participants, as well as a subsample (N = 533) identified as either cognitively unimpaired (CU) or mild cognitive impairment (MCI). Cross-validated area under the curve (cAUC) evaluated the predictive performance.
resultsThe best prediction model included age, sex, education, subjective memory concern, family history of Alzheimer's disease, Geriatric Depression Scale Short-Form, self-reported Everyday Cognition, and self-reported cognitive impairment. The cross-validated AUCs ranged from 0.62 to 0.66. This online model could help reduce between 15.2% and 23.7% of unnecessary Aβ PET scans in CU and MCI populations. DISUCSSION: The findings suggest that a novel, online approach could aid in Aβ prediction.
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What OpenQuestion holds
Registered trials
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.