Observational studyNutrients2022
Application of a Machine Learning Technology in the Definition of Metabolically Healthy and Unhealthy Status: A Retrospective Study of 2567 Subjects Suffering from Obesity with or without Metabolic Syndrome.
Observational study in Nutrients, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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
9 citing papers in PubMed.
- Machine learning with the body roundness index and associated indicators: a new approach to predicting metabolic syndrome.BMC public health · 2025Article
- Bridging the gap in obesity research: A consensus statement from the European Society for Clinical Investigation.European journal of clinical investigation · 2025Review
- Amino Acid Associations in Healthy and Unhealthy Obesity.The Journal of nutrition · 2025Article
- Uncovering Predictors of Lipid Goal Attainment in Type 2 Diabetes Outpatients Using Logic Learning Machine: Insights from the AMD Annals and AMD Artificial Intelligence Study Group.Journal of clinical medicine · 2023Article
- Transparent machine learning suggests a key driver in the decision to start insulin therapy in individuals with type 2 diabetes.Journal of diabetes · 2023Article
- Article
- Predictors of weight loss in patients with obesity treated with a Very Low-Calorie Ketogenic Diet.Frontiers in nutrition · 2023Article
- Machine learning insights concerning inflammatory and liver-related risk comorbidities in non-communicable and viral diseases.World journal of gastroenterology · 2022Review
- Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The key factors playing a role in the pathogenesis of metabolic alterations observed in many patients with obesity have not been fully characterized. Their identification is crucial, and it would represent a fundamental step towards better management of this urgent public health issue. This aim could be accomplished by exploiting the potential of machine learning (ML) technology. In a single-centre study (
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Identifiers
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.