ArticleAlzheimer's & dementia (Amsterdam, Netherlands)
Multilevel prediction of Alzheimer's disease dementia in the United States: An artificial intelligence analysis.
Article in Alzheimer's & dementia (Amsterdam, Netherlands). The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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.
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
1 citing paper in PubMed.
- Multilevel prediction of Alzheimer's disease dementia in the United States: An artificial intelligence analysis.Alzheimer's & dementia (Amsterdam, Netherlands)Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionVariables predicting Alzheimer's disease (AD) are not limited to individual-level risk factors. The purpose of this investigation is to assess multilevel predictors of AD prevalence.
methodsUS county-level datasets incorporating 45 predictor variables were analyzed cross-sectionally using artificial intelligence analytical methods. A Light Gradient-Boosting Machine model was trained to predict county-level AD after which model performance and feature importance were evaluated.
resultsThe final model retained 20 features and explained 75% ( DISCUSSION: This study confirmed upstream factors as being significant predictors of AD prevalence and racial and ethnic minority status as being the most important. From a policy perspective, efforts to reduce population levels of AD prevalence should consider addressing racial and ethnic disparities.
Indexed as
Identifiers
What Socratic 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 Socratic graph.