ArticlePloS one2021
A comparison of prediction approaches for identifying prodromal Parkinson disease.
Article in PloS one, 2021. 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.
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Who cites it
6 citing papers in PubMed, 12 citations in OpenAlex.
- A Narrative Review on Biochemical Markers and Emerging Treatments in Prodromal Synucleinopathies.Clinics and practice · 2025Review
- Balance and gait disorders in de novo Parkinson's disease: support for early rehabilitation.Journal of neurology · 2024Article
- Integrating Machine Learning into Statistical Methods in Disease Risk Prediction Modeling: A Systematic Review.Health data science · 2024Review
- A predictive algorithm to identify ever smoking in medical claims-based epidemiologic studies.Annals of epidemiology · 2023Article
- The implications of the foot health status in Parkinson patients: A case-control study.International wound journal · 2023Article
- Clinical Factors and Social Determinants of Health Predict Elder Mistreatment at Two Years Among Older Adults: A Preliminary Prediction Model.Inquiry : a journal of medical care organization, provision and financingObservational
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 at 2 institutions in 2 countries.
Funding
Abstract
Identifying people with Parkinson disease during the prodromal period, including via algorithms in administrative claims data, is an important research and clinical priority. We sought to improve upon an existing penalized logistic regression model, based on diagnosis and procedure codes, by adding prescription medication data or using machine learning. Using Medicare Part D beneficiaries age 66-90 from a population-based case-control study of incident Parkinson disease, we fit a penalized logistic regression both with and without Part D data. We also built a predictive algorithm using a random forest classifier for comparison. In a combined approach, we introduced the probability of Parkinson disease from the random forest, as a predictor in the penalized regression model. We calculated the receiver operator characteristic area under the curve (AUC) for each model. All models performed well, with AUCs ranging from 0.824 (simplest model) to 0.835 (combined approach). We conclude that medication data and random forests improve Parkinson disease prediction, but are not essential.
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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.