ArticleFrontiers in pediatrics2026
Identification of clinical phenotypes and prediction model for the mixed-infection phenotype of pediatric community-acquired pneumonia based on unsupervised machine learning.
Article in Frontiers in pediatrics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: Pediatric community-acquired pneumonia (CAP) exhibits significant clinical heterogeneity. Traditional microbiological classification overlooks host factors, making it challenging to accurately determine prognosis and provide targeted, precise treatment. Based on unsupervised machine learning, this study integrates microbiological, host inflammatory response, and clinical characteristics to phenotype pediatric CAP and develops an early prediction model for the Mixed-Infection phenotype. Methods: A retrospective cohort of 305 pediatric patients with CAP who underwent bronchoalveolar lavage (BAL) was included between November 2022 and October 2025. Using microbiological evidence from BAL fluid, inflammatory markers, and clinical features, k-prototypes clustering was applied to identify and classify phenotypes. A decision tree and nomogram were developed to predict the Mixed-Infection phenotype. Results: Three clinical phenotypes were identified through machine learning: Mycoplasma-Dominant (37.7%), characterized by Mycoplasma infection with moderate inflammatory response; Mixed-Infection (28.2%), characterized by multi-pathogen coinfection, the youngest age group, and the most extended hospital stays; and High-Inflammation (34.1%), characterized by elevated CRP and WBC levels. The Mixed-Infection phenotype had the highest proportion of prolonged hospitalization (31.4%). However, this difference did not reach statistical significance ( Conclusion: This study systematically applied k-prototypes clustering to identify three clinical phenotypes, revealing distinct "pathogen-host" interaction patterns among them. We developed a simple early identification tool for the Mixed-Infection phenotype. However, our findings are derived from a bronchoscopy/BAL-selected cohort with more severe or complex disease, which may limit generalizability to all pediatric CAP patients. While this tool shows significant potential, further validation in larger prospective cohorts is needed to confirm its generalizability and clinical applicability.
Indexed as
Identifiers
What OpenQuestion holds
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.