ArticleDiscover oncology2025
Nomogram predicting overall survival in hospitalized cervical cancer patients in Mato Grosso, Brazil.
Article in Discover oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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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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Who cites it
1 citing paper in PubMed.
- A Novel Clinical Nomogram for Predicting Unfavorable Tuberculosis Treatment Outcomes: A Logistic Regression Risk Model.Journal of epidemiology and global health · 2026Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionNomograms are widely recognized as effective predictive tools for estimating cancer prognosis, providing a personalized and practical approach to support clinical decision-making. This study aimed to develop and validate a nomogram for predicting the survival of hospitalized patients with cervical cancer (CC).
methodsEligible data were obtained from the Hospital Information System (SIH) of Brazil's Unified Health System (SUS) in Mato Grosso State, covering the period from 2011 to 2023. A nomogram was constructed based on a previously published multivariable Cox regression model. Model performance was assessed using Harrell's concordance index (C-index) and a calibration curve.
resultsThe developed nomogram achieved a C-index of 0.817, indicating good discriminative ability. The most significant predictors included the type of medical procedure performed, the need for ICU admission, and hospital costs. The calibration curve demonstrated good agreement between actual and predicted 30-day survival probabilities.
conclusionA useful clinical nomogram was developed to calculate the probability of survival for hospitalized patients with CC. The model demonstrated excellent performance, assisting healthcare professionals in selecting more appropriate treatments and providing accurate prognostic predictions for both clinical and research contexts.
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