ArticleJournal of robotic surgery2025
Construction of a risk factor prediction model for postoperative complications in elderly patients with colorectal cancer using machine learning.
Article in Journal of robotic surgery, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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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.
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Who cites it
3 citing papers in PubMed.
- Review
- Critique on construction of a risk factor prediction model of postoperative complications of elderly patients with colorectal cancer using machine learning.Journal of robotic surgery · 2025Article
- Selection bias, overfitting, and generalisability in predicting postoperative complications: insights from Fang et al.'s work.Journal of robotic surgery · 2025Article
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study aimed to assess the impact of various clinical and surgical factors on the occurrence of postoperative complications in elderly patients with colorectal cancer and to use various machine learning models to predict the risk of these complications. This study included 109 elderly patients with colorectal cancer who underwent robot-assisted surgery and natural orifice specimen extraction (NOSE) at the Second Xiangya Hospital of Central South University from March 2016 to March 2024. The patients were divided into a no-complication group (77 patients) and a complication group (32 patients) based on whether complications occurred within 30 days after surgery. The clinical and surgical data were collected and analysed using various machine learning models to evaluate the importance of each variable, and SHAP values were used to interpret the model's predictions. The average age of patients in the complication group was significantly greater than that in the no-complication group (77.75 vs. 72.55 years, P < 0.001), and their BMI was also greater (24.22 vs. 22.51 kg/m
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Registered trials
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