ArticleAnnals of medicine2026
Preoperative prediction of acute severe cholecystitis using an attentive interpretable tabular network (TabNet)-based radiomics model and a stacking ensemble: a two-center study.
Article in Annals of medicine, 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
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesTo develop an explainable and cost-effective predictive model for acute severe cholecystitis (ASC) utilizing stacking ensemble learning framework and SHapley Additive exPlanations (SHAP) algorithm.
methodsThis retrospective study was conducted on 492 patients with pathologically confirmed acute cholecystitis, collected from two tertiary hospitals between January 2020 and January 2023. The analysis was performed in December 2025. The patients divided into a training set (
resultsIn five-fold cross-validation, the stacking model achieved a mean area under the curve (AUC) of 0.850, surpassing standalone TabNet (0.807) and XGBoost (0.799). This superiority was maintained in the external test set (AUC: 0.827 vs. 0.753 and 0.792). In the external test set, the stacking model also yielded the lowest Brier score (0.156) and the highest clinical net benefit in decision curve analysis. SHAP analysis identified neutrophil percentage, gallbladder wall necrosis, and pericholecystic exudation as the most influential clinical predictors, with radiomic features providing a higher overall weight in the final ensemble.
conclusionsThe interpretable stacking model effectively integrates clinical and radiomic data to accurately predict ASC preoperatively.
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.