ArticleFrontiers in public health2025
Building a diagnostic prediction model for severe
Article in Frontiers in public health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 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
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine learning-based prediction models for severeFrontiers in public health · 2026Pooled it
- Artificial Intelligence-Assisted Pathogen Detection: Algorithms, Biosensing Platforms, and Applications.Biosensors · 2026Review
- Network analysis of Mycoplasma pneumoniae pneumonia and Epstein-Barr virus co-infection.BMC infectious diseases · 2026Article
- Article
- Early prediction of plastic bronchitis in pediatric patients withFrontiers in cellular and infection microbiology · 2026Article
- Nomogram to predict severeFrontiers in pediatrics · 2026Article
- Machine learning-driven risk assessment of severeFrontiers in medicine · 2026Article
- Explainable machine learning for diagnosing severeFrontiers in pediatrics · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: Methods: We collected medical records from 372 MPP cases. We compared case characteristics between groups with and without SMPP and used a random forest to screen key factors. We then constructed a multivariate logistic prediction model. We evaluated the model with ROC curves, calibration curves, and DCA. Five-fold cross-validation tested prediction stability. Results: We identified ESR, PCT, IL-6, and lung auscultation as key factors to construct the prediction model. The model's ROC was 0.964 (95% CI: 0.945-0.983). Calibration curves and DCA confirmed model accuracy. Five-fold cross-validation validated internal stability. Conclusion: Our study developed a prediction model with good efficacy for early SMPP risk assessment. Our research provides a basis for clinical early prediction and prevention of SMPP, reducing its risk and offering a foundation for individualized treatment and improved long-term outcomes in affected 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.