ArticleiScience2024
Predictive modeling for eosinophilic chronic rhinosinusitis: Nomogram and four machine learning approaches.
Article in iScience, 2024. 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.
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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
6 citing papers in PubMed, 7 citations in OpenAlex.
- Construction and validation of a prognostic model for CRSwNP.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2026Article
- Predicting mild cognitive impairment among rural older adults in China: development and validation of a risk prediction model.BMC geriatrics · 2026Article
- How to Identify Type 2 Chronic Rhinosinusitis in East Asian Patients.Journal of rhinology : official journal of the Korean Rhinologic Society · 2026Review
- Artificial intelligence-assisted type 2 inflammatory endotyping in CRSwNP: from Sinus CT and digital pathology to biologic decision support.Frontiers in allergy · 2026Review
- Artificial intelligence optimizes the standardized diagnosis and treatment of chronic sinusitis.Frontiers in physiology · 2025Review
- Development and validation of a collagen signature to predict central lymph node metastasis in papillary thyroid cancer.Frontiers in endocrinology · 2025Article
Corrections and comments
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Authors and funding
10 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Eosinophilic chronic rhinosinusitis (ECRS) is a distinct subset of chronic rhinosinusitis characterized by heightened eosinophilic infiltration and increased symptom severity, often resisting standard treatments. Traditional diagnosis requires invasive histological evaluation. This study aims to develop predictive models for ECRS based on patient clinical parameters, eliminating the need for invasive biopsy. Utilizing logistic regression with lasso regularization, random forest (RF), gradient-boosted decision tree (GBDT), and deep neural network (DNN), we trained models on common clinical data. The predictive performance was evaluated using metrics such as area under the curve (AUC) for receiver operator characteristics, decision curves, and feature ranking analysis. In a cohort of 437 eligible patients, the models identified peripheral blood eosinophil ratio, absolute peripheral blood eosinophil, and the ethmoidal/maxillary sinus density ratio (E/M) on computed tomography as crucial predictors for ECRS. This predictive model offers a valuable tool for identifying ECRS without resorting to histological biopsy, enhancing clinical decision-making.
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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.