ArticleJournal of Parkinson's disease2020
Quadruple Decision Making for Parkinson's Disease Patients: Combining Expert Opinion, Patient Preferences, Scientific Evidence, and Big Data Approaches to Reach Precision Medicine.
Article in Journal of Parkinson's disease, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 2 of them syntheses that pooled it.
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
17 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Data-driven decision making in patient management: a systematic review.BMC medical informatics and decision making · 2025Pooled it
- Translating Evidence to Advanced Parkinson's Disease Patients: A Systematic Review and Meta-Analysis.Movement disorders : official journal of the Movement Disorder Society · 2021Pooled it
- Client Perceptions of the Individual Packer Managing Fatigue Program: A Mixed-Method Evaluation.OTJR : occupation, participation and health · 2024Trial
- Long-Term Effectiveness and Safety of LCIG in Romania. 3-year Results from a Prospective, Observational Study (DUOGLOBE).Neuropsychiatric disease and treatment · 2026Article
- Big data for neuroscience in the context of predictive, preventive, and personalized medicine.The EPMA journal · 2025Review
- The triglyceride-glucose index and its obesity-related derivatives as predictors of all-cause and cardiovascular mortality in hypertensive patients: insights from NHANES data with machine learning analysis.Cardiovascular diabetology · 2025Article
- Tackling vascular risk factors as a possible disease modifying intervention in Parkinson's disease.NPJ Parkinson's disease · 2024Article
- Delivering Multidisciplinary Rehabilitation Care in Parkinson's Disease: An International Consensus Statement.Journal of Parkinson's disease · 2024Article
- Challenges and best practices for digital unstructured data enrichment in health research: A systematic narrative review.PLOS digital health · 2023Article
- The evolution of Big Data in neuroscience and neurology.Journal of big data · 2023Article
- Perspectives of people living with Parkinson's disease on personalized prediction models.Health expectations : an international journal of public participation in health care and health policy · 2022Article
- Functional capacity vs side effects: treatment attributes to consider when individualising treatment for patients with rheumatoid arthritis.Clinical rheumatology · 2022Article
- Digital assessment at home - mPower against Parkinson disease.Nature reviews. Neurology · 2021Article
- Estimating the Effect of Early Treatment Initiation in Parkinson's Disease Using Observational Data.Movement disorders : official journal of the Movement Disorder Society · 2021Article
- Review
- Integrated and patient-centred management of Parkinson's disease: a network model for reshaping chronic neurological care.The Lancet. Neurology · 2020Review
- A Radiomics Approach to Predicting Parkinson's Disease by Incorporating Whole-Brain Functional Activity and Gray Matter Structure.Frontiers in neuroscience · 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
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Clinical decision making for Parkinson's disease patients is supported by a combination of three distinct information resources: best available scientific evidence, professional expertise, and the personal needs and preferences of patients. All three sources have clear value but also share several important limitations, mainly regarding subjectivity, generalizability and variability. For example, current scientific evidence, especially from controlled clinical trials, is often based on selected study populations, making it difficult to translate the outcome to the care for individual patients in everyday clinical practice. Big data, including data from real-life unselected Parkinson populations, can help to bridge this information gap. Fine-grained patient profiles created from big data have the potential to aid in identifying therapeutic approaches that will be most effective given each patient's individual characteristics, which is particularly important for a disorder characterized by such tremendous interindividual variability as Parkinson's disease. In this viewpoint, we argue that big data approaches should be acknowledged and harnessed, not to replace existing information resources, but rather as a fourth and complimentary source of information in clinical decision making, helping to represent the full complexity of individual patients. We introduce the 'quadruple decision making' model and illustrate its mode of action by showing how this can be used to pursue precision medicine for persons living with Parkinson's disease.
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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.