ArticleJournal of inflammation research2024
Construction and Validation of a Nomogram Model to Predict the Severity of
Article in Journal of inflammation research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 1 of them a synthesis that pooled it.
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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.
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.
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Who cites it
29 citing papers in PubMed, 1 synthesis or guideline pooled it, 25 citations in OpenAlex.
- Machine learning-based prediction models for severeFrontiers in public health · 2026Pooled it
- Application of Clinical Indicators in the Early Prediction of RefractoryClinical pediatrics · 2026Article
- A machine learning-based early in-hospital risk stratification model for severe mycoplasma pneumoniae pneumonia in children.Translational pediatrics · 2026Article
- Multi-Omics Profiling Identifies Immune-Metabolic Signatures and Gut Microbial Biomarkers in a Murine Model ofBiology · 2026Article
- Mapping the Evolution of Diagnostic Research onPathogens (Basel, Switzerland) · 2026Review
- Development and validation of an interpretable machine learning model using routine laboratory biomarkers to stratify severe pneumonia risk in young children.Journal of advanced research · 2026Article
- Routine admission biomarkers for identifying co-infection and stratifying severity in pediatric macrolide-resistantTranslational pediatrics · 2026Article
- Methodological comparison of capillary and venous blood parameters in pediatricTranslational pediatrics · 2026Article
- Impact of pathogen co-detection on disease severity and clinical outcomes in children with Mycoplasma pneumoniae pneumonia.World journal of pediatrics : WJP · 2026Article
- Association between normalizedTranslational pediatrics · 2026Article
- Impact of Allergic Diseases or Obstructive Sleep Apnea Risk on SevereJournal of clinical medicine · 2026Article
- Independent risk factors and predictive model for high hospitalization costs in children with Mycoplasma pneumoniae pneumonia.BMC pediatrics · 2026Article
- Host immune dysfunction and Mycoplasma pneumoniae immune evasion: dual drivers of pathogenesis in Mycoplasma pneumoniae pneumonia.Inflammation research : official journal of the European Histamine Research Society ... [et al.] · 2026Review
- [Development of a predictive model for severeZhongguo dang dai er ke za zhi = Chinese journal of contemporary pediatrics · 2026Article
- Nomogram to predict severeFrontiers in pediatrics · 2026Article
- Machine learning-driven risk assessment of severeFrontiers in medicine · 2026Article
- A Novel Nomogram Integrating Clinical, Radiological, and Immunological Indicators for Early Prediction of SevereJournal of inflammation research · 2026Article
- Host inflammatory markers and MLVA profiling for risk assessment of severe pediatricFrontiers in cellular and infection microbiology · 2026Article
- Dynamic multiplex cytokine profiling to identify risk factors for Lung Consolidation and Necrotizing Transformation in Children withMediterranean journal of hematology and infectious diseases · 2026Article
- Characteristics and risk factors for outcomes in patients withInfectious Disease Modelling · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: This study aimed to develop a nomogram model for early prediction of the severe Methods: A retrospective analysis was conducted on children with MPP, classifying them into severe and general MPP groups. The risk factors for severe MPP were identified using Logistic Stepwise Regression Analysis, followed by Multivariate Regression Analysis to construct the nomogram model. The model's discrimination was evaluated using a receiver operating characteristic curve, its calibration with a calibration curve, and the results were visualized using the Hosmer-Lemeshow goodness-of-fit test. Results: Univariate analysis revealed that age, duration of fever, length of hospital-stay, decreased sounds of breathing, respiratory rate, hypokalemia, and incidence of co-infection were significantly different between severe and general MPP. Significant differences ( Conclusion: Age, decreased sound of breathing, respiratory rate, duration of fever, length of hospital-stay, co-infection with other pathogen(s), ferritin level, and LDH level were the significant contributors for the establishment of a nomogram model to predict the severity of MPP in children.
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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.