{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T07:00:14Z","timestamp":1780470014266,"version":"3.54.1"},"reference-count":49,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T00:00:00Z","timestamp":1780444800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Public Health"],"abstract":"<jats:sec>\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>\n                      This study aimed to systematically evaluate the predictive performance and methodological characteristics of machine learning\u2013based models for predicting progression to severe\n                      <jats:italic>Mycoplasma pneumoniae<\/jats:italic>\n                      pneumonia (SMPP) in pediatric patients.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A comprehensive literature search was conducted in PubMed, EMBASE, Web of Science, Cochrane Library, CNKI, and Wanfang databases from inception to November 2025 to identify studies developing or validating prediction models for SMPP in children. Data on study characteristics, modeling algorithms, predictors, and performance metrics were extracted. A narrative synthesis was performed to summarize model characteristics, predictors, and modeling approaches, while model discrimination was quantitatively synthesized using pooled area under the receiver operating characteristic curve (AUC). Subgroup analyses were conducted according to modeling algorithms. Methodological quality and risk of bias were assessed using the PROBAST tool.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>A total of 13 studies were included. The reported prevalence of hypoglycemia ranged from 17 to 33%. The AUC for predictive models ranged from 0.81 to 0.90. Subgroup analyses showed that machine learning-based models such as XGBoost and random forest generally reported higher AUC values compared with other modeling approaches. Commonly reported predictors included age, insulin use, body mass index, HbA1c, creatinine, and history of hypoglycemia.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Research on risk prediction models for SMPP in children is still at a developmental stage. Although current models demonstrate high discriminatory performance, methodological limitations and limited clinical translation remain. Future studies should focus on developing robust, interpretable machine learning models and facilitating their integration into pediatric clinical practice.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Systematic review registration<\/jats:title>\n                    <jats:p>\n                      <jats:ext-link>https:\/\/www.crd.york.ac.uk\/PROSPERO\/<\/jats:ext-link>\n                      CRD42020190338.\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fpubh.2026.1793116","type":"journal-article","created":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T06:00:30Z","timestamp":1780466430000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Machine learning-based prediction models for severe Mycoplasma pneumoniae pneumonia in Chinese children: a systematic review and meta-analysis of prediction model performance"],"prefix":"10.3389","volume":"14","author":[{"given":"Juan","family":"Cao","sequence":"first","affiliation":[{"name":"College of Medicine and Health Sciences, China Three Gorges University","place":["Yichang, Hubei, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiao","family":"Nie","sequence":"additional","affiliation":[{"name":"Department of Nursing, Jiangxi Provincial Children\u2019s Hospital","place":["Nanchang, Jiangxi, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Danxia","family":"Wu","sequence":"additional","affiliation":[{"name":"Department of Respiratory Medicine, Jiangxi Provincial Children\u2019s Hospital","place":["Nanchang, Jiangxi, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huiqin","family":"Liu","sequence":"additional","affiliation":[{"name":"The First College of Clinical Medicine, China Three Gorges University, Yichang Central People\u2019s Hospital","place":["Yichang, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,6,3]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"421","DOI":"10.1002\/jmv.24963","article-title":"Etiology of community-acquired pneumonia in 1500 hospitalized children","volume":"90","author":"Oumei","year":"2018","journal-title":"J Med Virol"},{"key":"ref2","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1016\/j.prrv.2022.01.006","article-title":"Systematic review of clinical practice guidelines on the management of community acquired pneumonia in children","volume":"42","author":"Chee","year":"2022","journal-title":"Paediatr Respir Rev"},{"key":"ref3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1002\/ped4.12469","article-title":"Evidence-based guideline for the diagnosis and treatment of Mycoplasma pneumoniae pneumonia in children (2023)","volume":"9","year":"2025","journal-title":"Pediatr 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