ArticleNeuro-ophthalmology (Aeolus Press)2018
The Use of a Nomogram to Visually Interpret a Logistic Regression Prediction Model for Giant Cell Arteritis.
Article in Neuro-ophthalmology (Aeolus Press), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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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
14 citing papers in PubMed, 1 synthesis or guideline pooled it, 24 citations in OpenAlex.
- Systematic Review of Privacy-Preserving Distributed Machine Learning From Federated Databases in Health Care.JCO clinical cancer informatics · 2020Pooled it
- Development and validation of an online nomogram to predict breast cancer screening participation among high-risk women: A cross-sectional study.Asia-Pacific journal of oncology nursing · 2026Article
- Review
- Applications of Artificial Intelligence as a Prognostic Tool in the Management of Acute Aortic Syndrome and Aneurysm: A Comprehensive Review.Journal of clinical medicine · 2025Review
- Development of a machine learning-based predictive nomogram for screening children with juvenile idiopathic arthritis: a pseudo-longitudinal study of 223,195 children in the United States.Frontiers in public health · 2025Article
- Development and validation of risk prediction model for adverse outcomes in trauma patients.Annals of medicine · 2024Article
- Machine Learning Analysis of Factors Influencing Pediatric Telehealth Visits During COVID-19: A State-Level Comparison Using 2021-22 National Survey of Children's Health Data.Healthcare (Basel, Switzerland) · 2024Article
- Clinical, biological, and ophthalmological characteristics differentiating arteritic from non-arteritic anterior ischaemic optic neuropathy.Eye (London, England) · 2023Article
- Multiple regression analysis of risk factors related to radiation pneumonitis.World journal of clinical cases · 2023Article
- Incidence and Predictors of Synchronous Bone Metastasis in Newly Diagnosed Differentiated Thyroid Cancer: A Real-World Population-Based Study.Frontiers in surgery · 2022Article
- Prediction of adolescent suicidal ideation after the COVID-19 pandemic: A nationwide survey of a representative sample of Korea.Frontiers in pediatrics · 2022Article
- A nomogram to predict rupture risk of middle cerebral artery aneurysm.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2021Article
- Systematic Review of the Yield of Temporal Artery Biopsy for Suspected Giant Cell Arteritis.Neuro-ophthalmology (Aeolus Press) · 2019Review
- Neural network and logistic regression diagnostic prediction models for giant cell arteritis: development and validation.Clinical ophthalmology (Auckland, N.Z.) · 2019Article
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo illustrate the utility of a nomogram for the prediction of giant cell arteritis (GCA).
methodA nomogram was constructed from a multivariable logistic regression prediction model with 10 covariates: age, sex, clinical temporal artery abnormality, new-onset headache, jaw claudication, vision loss, diplopia, erythrocyte sedimentation rate, C-reactive protein, and platelet level.
resultsThe magnitude and location of the nomogram scale for each predictor variable graphically illustrates the net effect of each covariate and is especially useful for continuous variables such as age and bloodwork values.
conclusionsNomograms allow integration and synthesis of the relative importance of clinical variables and provide a graphic representation of the odds ratios,
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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.