ArticleFrontiers in surgery2026
Development of an automated machine learning-based risk prediction and decision support system for postoperative cubitus varus complicating pediatric lateral humeral condyle fracture.
Article in Frontiers in surgery, 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
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: Postoperative cubitus varus is a disabling complication of pediatric lateral humeral condyle fractures that impairs long-term elbow function. This study aimed to develop and validate the first Improved LangEvin Equation-based Evolutionary (ILEE) automated machine learning (AutoML) model and visual decision support system for preoperative prediction of this complication, to enable personalized risk stratification and targeted intervention. Methods: We conducted a retrospective cohort study of 330 children with lateral humeral condyle fractures treated between January 2005 and June 2022. An ILEE-optimized AutoML model was constructed and compared with the original LEE algorithm and 6 conventional machine learning models. Model performance was evaluated using area under the receiver operating characteristic curve (ROC-AUC), accuracy, sensitivity, and specificity. Key predictors were identified and interpreted via SHapley Additive exPlanations (SHAP) analysis, and a clinical decision support system was developed using MATLAB App Designer. Results: The ILEE algorithm outperformed all comparators in optimization stability and convergence speed. The AutoML model identified five key features for predicting cubitus varus and exhibited better prediction calibration performance compared to other models (ROC-AUC = 0.9557). SHAP analysis ranked the importance of these features as follows: obesity degree, preoperative timing, internal fixation time, fracture type, and external fixation time. The decision support system can generate real-time risk levels, predicted probabilities, and personalized clinical recommendations within 1 s. Conclusion: This study establishes the first ILEE-based AutoML model for predicting postoperative cubitus varus in pediatric lateral humeral condyle fractures, demonstrating competitive predictive performance. The user-friendly visual system enables rapid preoperative risk assessment, assisting clinicians in identify high-risk patients, optimize treatment plans, and potentially improving pediatric orthopedic outcomes.
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.