ArticleSurgical endoscopy2026
Predicting long-term survival after gastrectomy in elderly patients with esophagogastric junction adenocarcinomas.
Article in Surgical endoscopy, 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
9 authors.
Funding
Abstract
backgroundThe long-term survival outcomes of older patients with esophagogastric junction adenocarcinoma (EGJA) remain unclear. This study aimed to develop and validate a machine learning model to predict postoperative survival in patients aged ≥ 75 years with EGJA.
methodsThis retrospective study analyzed patients aged ≥ 75 years who underwent surgical resection for EGJA between 2009 and 2018 across nine tertiary hospitals in China. A random survival forest (RSF) model was developed to predict overall survival (OS) and disease-free survival (DFS) using key clinicopathological variables. Model performance was compared with traditional Cox proportional hazards (CPH) regression and TNM staging.
resultsThe study included 1,202 patients. In the training set, the RSF model demonstrated superior discriminatory ability for 5-year OS (time-dependent AUC: 0.887 vs. 0.78) and DFS (0.836 vs. 0.767) compared to the CPH model. The RSF model also showed better calibration with higher C-index (CiD) for OS (0.723 vs. 0.704) and DFS (0.746 vs. 0.701). In the test set, the RSF model maintained robust performance, with CiD values of 0.719 for OS and 0.738 for DFS, outperforming the CPH model (0.704 and 0.697, respectively). In the independent external validation cohort, the RSF model maintained favorable performance and outperformed the CPH model for OS and DFS prediction, with CiD values of 0.720 for OS and 0.711 for DFS, compared with 0.687 and 0.679 for the CPH model, respectively. The RSF model exhibited particularly strong predictive power for both OS and DFS between 12 and 36 months post-surgery. Key predictors included pN stage, pT stage, age, and age-adjusted Charlson Comorbidity Index.
conclusionThe machine learning-based RSF model provides individualized and accurate survival predictions for older patients with EGJA after surgical resection. This model may assist clinicians in postoperative risk stratification and follow-up planning.
Indexed as
Identifiers
42816638What 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.