ReviewInternational journal of obesity (2005)2016
The importance of prediction model validation and assessment in obesity and nutrition research.
Review in International journal of obesity (2005), 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers, 1 of them a synthesis that pooled it.
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
38 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A systematic review of dengue outbreak prediction models: Current scenario and future directions.PLoS neglected tropical diseases · 2023Pooled it
- Trial
- Early Antenatal Prediction of Gestational Diabetes in Obese Women: Development of Prediction Tools for Targeted Intervention.PloS one · 2016Trial
- Machine Learning and Artificial Intelligence in Nutrition Research: Analytical Methods, Applications, and Key Considerations.The Journal of nutrition · 2026Review
- Clinical guideline-guided prognostic models of functional disability after acute ischemic stroke: a retrospective cohort study.BMC neurology · 2026Article
- From Population-Based PBPK to Individualized Virtual Twins: Clinical Validation and Applications in Medicine.Journal of clinical medicine · 2026Review
- Generalizability of a Musculoskeletal Therapist Electronic Health Record for Modelling Outcomes to Work-Related Musculoskeletal Disorders.Journal of occupational rehabilitation · 2025Article
- Prognostic Models of Mortality Following First-Ever Acute Ischemic Stroke: A Population-Based Retrospective Cohort Study.Health science reports · 2025Article
- Patient perspective on predictive models in healthcare: translation into practice, ethical implications and limitations?BMJ health & care informatics · 2025Article
- Publicly available datasets of breast histopathology H&E whole-slide images: A scoping review.Journal of pathology informatics · 2024Article
- The Development of a Resting Metabolic Rate Prediction Equation for Professional Male Rugby Union Players.Nutrients · 2024Article
- Derivation of a risk-adjusted model to predict antibiotic prescribing among hospitalists in an academic healthcare network.Antimicrobial stewardship & healthcare epidemiology : ASHE · 2024Article
- Covariate dependent Markov chains constructed with gradient boost modeling can effectively generate long-term predictions of obesity trends.BMC research notes · 2023Article
- Leakage and the reproducibility crisis in machine-learning-based science.Patterns (New York, N.Y.) · 2023Article
- Prediction-oriented prognostic biomarker discovery with survival machine learning methods.NAR genomics and bioinformatics · 2023Article
- Validation of multiparametric MRI based prediction model in identification of pseudoprogression in glioblastomas.Journal of translational medicine · 2023Article
- On the Best Way to Cluster NCI-60 Molecules.Biomolecules · 2023Article
- Article
- CARRoT: R-package for predictive modelling by means of regression, adjusted for multiple regularisation methods.PloS one · 2023Article
- Valuing the Diversity of Research Methods to Advance Nutrition Science.Advances in nutrition (Bethesda, Md.) · 2022Article
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
Abstract
Deriving statistical models to predict one variable from one or more other variables, or predictive modeling, is an important activity in obesity and nutrition research. To determine the quality of the model, it is necessary to quantify and report the predictive validity of the derived models. Conducting validation of the predictive measures provides essential information to the research community about the model. Unfortunately, many articles fail to account for the nearly inevitable reduction in predictive ability that occurs when a model derived on one data set is applied to a new data set. Under some circumstances, the predictive validity can be reduced to nearly zero. In this overview, we explain why reductions in predictive validity occur, define the metrics commonly used to estimate the predictive validity of a model (for example, coefficient of determination (R(2)), mean squared error, sensitivity, specificity, receiver operating characteristic and concordance index) and describe methods to estimate the predictive validity (for example, cross-validation, bootstrap, and adjusted and shrunken R(2)). We emphasize that methods for estimating the expected reduction in predictive ability of a model in new samples are available and this expected reduction should always be reported when new predictive models are introduced.
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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.