Observational studyInternational urology and nephrology2026
Evaluation of explainable machine learning models for predicting mid-term stone recurrence after percutaneous nephrolithotomy: a retrospective observational cohort study.
Observational study in International urology and nephrology, 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
objectiveTo develop an explainable machine learning (ML) model to predict mid-term (3 years) stone recurrence (SR) after percutaneous nephrolithotomy (PCNL). This single-center retrospective observational cohort study compared nonlinear algorithms with logistic regression (LR) and evaluated the predictive value of systemic inflammatory markers, clinical and stone-related features. MATERIALS AND
methodsWe retrospectively analyzed 412 PCNL patients with complete preoperative and 3-year follow-up data between 2014 and 2021. The patients were split chronologically into 70% training and 30% independent test sets. Random Forest (RF), Gradient Boosting Machine (GBM), Extreme Gradient Boosting (XGBoost), and LR models were trained using clinical, stone, and hematologic parameters, including inflammatory indices. Model performance and interpretability were evaluated using the area under the receiver operating characteristic curve (AUROC), accuracy, sensitivity, specificity, Cohen's kappa, and SHapley Additive exPlanations (SHAP).
resultsThe RF model demonstrated the highest predictive performance (AUROC:0.92, accuracy:88%, kappa:0.773, p < 0.001) in the test set and outperformed the other algorithms. Residual stone size (RES) > 3 mm was the strongest predictor of SR (sensitivity, 96%; specificity, 72%). Inflammatory markers had limited independent predictive value. Decision curve analysis showed the net clinical benefit of RF, and SHAP analysis identified stone burden and RES as the most influential features.
conclusionAn explainable RF model effectively predicted 3-year recurrence after PCNL and emphasized the importance of achieving RES ≤ 3 mm. Although inflammatory markers contributed little to the prediction, this model has potential to enable personalized risk assessment to guide postoperative care if it is externally validated in multicenter cohorts before clinical implementation.
Indexed as
Identifiers
41880132What 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.