Evidence map›Paper›PMID 41240233›Full record

ArticleEuropean journal of clinical microbiology & infectious diseases : official publication of the European Society of Clinical Microbiology2026

Predicting macrolide resistance in pediatric Mycoplasma pneumoniae pneumonia: A machine learning modeling study.

Shuo Yang, Xinying Liu, Huizhe Wang, Yaowei Han, Dan Sun, Huanmin Li, Liting Ma, Haokai Wang, Xinmin Li

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Article in European journal of clinical microbiology & infectious diseases : official publication of the European Society of Clinical Microbiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Early identification of refractoryFrontiers in medicine · 2026
    Article
4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Shuo YangFirst Teaching Hospital of Tianjin University of Traditional Chinese Medicine, Tianjin, 300381, China.
Xinying LiuFirst Teaching Hospital of Tianjin University of Traditional Chinese Medicine, Tianjin, 300381, China.
Huizhe WangFirst Teaching Hospital of Tianjin University of Traditional Chinese Medicine, Tianjin, 300381, China.
Yaowei HanFirst Teaching Hospital of Tianjin University of Traditional Chinese Medicine, Tianjin, 300381, China.
Dan SunFirst Teaching Hospital of Tianjin University of Traditional Chinese Medicine, Tianjin, 300381, China.
Huanmin LiFirst Teaching Hospital of Tianjin University of Traditional Chinese Medicine, Tianjin, 300381, China.
Liting MaFirst Teaching Hospital of Tianjin University of Traditional Chinese Medicine, Tianjin, 300381, China.
Haokai WangFirst Teaching Hospital of Tianjin University of Traditional Chinese Medicine, Tianjin, 300381, China.
Xinmin LiFirst Teaching Hospital of Tianjin University of Traditional Chinese Medicine, Tianjin, 300381, China. tjlxmtcm@163.com.

Funding

National Natural Science Foundation of China 82575140
6 · The paper itself

Abstract

purposeTo develop a machine learning-based clinical prediction model for macrolide-resistant Mycoplasma pneumoniae pneumonia (MRMPP) in children, facilitating early identification of resistant cases and guiding targeted therapeutic interventions.

methodsIn this retrospective, single-center study, we developed a stacking ensemble prediction model using demographic, laboratory, and inflammatory data from pediatric patients with MPP. A feature selection protocol was implemented to identify key predictors. The final model was validated using both internal cross-validation and an independent external temporal cohort. Model interpretability was assessed using SHapley Additive exPlanations (SHAP).

resultsThe stacking ensemble model achieved an area under the curve (AUC) of 0.857 during internal validation, with a sensitivity of 0.769 and specificity of 0.841; the AUC during external validation was 0.812. Key predictive factors included interleukin-17 A (IL-17 A), interferon-gamma (IFN-γ), C-reactive protein (CRP), albumin-to-globulin ratio (A/G), History of pre-hospital macrolide use, and Pre-hospital course. The model is implemented as a web tool, facilitating rapid assessment of resistance risk.

conclusionThe machine learning model developed in this study can initially identify children at high risk for MRMPP, serving as a data-driven decision-making tool for the rational use of antibiotics in clinical practice and demonstrating significant clinical translational value.

Indexed as

Anti-Bacterial AgentsDrug Resistance, BacterialMachine LearningMacrolidesMycoplasma pneumoniaePneumonia, MycoplasmaAdolescentChildChild, PreschoolFemaleHumansInfantMaleRetrospective StudiesAnti-Bacterial AgentsMacrolidesChildMachine learningMacrolide resistancePneumonia, MycoplasmaPredictive learning modelsStacking ensemble

Identifiers

PMID41240233

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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.