Evidence map›Paper›PMID 42185305›Full record

Trial reportNature communications2026

Machine learning model-guided selective use of temporary diverting ileostomy in rectal cancer surgery: a randomized controlled trial.

Shengli Shao, Yanqi Li, Jianghao Li, Lu Liu, Ying Zeng, Chunyue Song, Wenjun Huang, Lei Mu, Jichao Qin

Erratum issued Registry-linked trialAbstract readRandomized Controlled Trial
PubMed Full text at PMC Publisher
In one paragraph

Trial report in Nature communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. It is linked to trial NCT04999007 (Artificial Intelligence-assisted Decision Making for Temporary Ileostomy), which is not on this map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

NCT04999007 narecruitingnot on this map

Artificial Intelligence-assisted Decision Making for Temporary Ileostomy: A Prospective Randomized Controlled Trail.

TypeinterventionalSponsorJichao QinRan2021 to 2025Enrolled616ConditionsRectal CancerArmsArtificial intelligence algorithm
3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

9 authors.

Shengli Shao *Department of Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei Province, China.
Yanqi Li *Department of Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei Province, China.
Jianghao Li *Department of Gastrointestinal Surgery, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Lu LiuDepartment of Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei Province, China.
Ying ZengDepartment of Nursing, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Chunyue SongInstitute of Industry Intelligence and Systems Engineering, College of Control Science and Engineering, Zhejiang University, Hangzhou, China.
Wenjun HuangZhejiang University, Hangzhou, China.
Lei MuDepartment of Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei Province, China.
Jichao QinDepartment of Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei Province, China. jcqin2024@zju.edu.cn.ORCID http://orcid.org/0000-0002-2961-7624

Funding

National Natural Science Foundation of China (National Science Foundation of China) nos. 82173368 and 82372995
6 · The paper itself

Abstract

The appropriate use of temporary diverting ileostomy (TDI) in rectal cancer surgery lacks standardized criteria. A randomized controlled trial was performed to evaluate whether the Risk-Guided Temporary Ileostomy Decision (RTID) system, a machine learning-based anastomotic leakage (AL) prediction model, could improve the suitability of TDI utilization. A total of 872 patients with stage I-III rectal cancer undergoing anterior resection were randomized 1:1 to surgeon discretion versus RTID-guided decisions. The final analysis included 750 patients (control, n = 368; RTID, n = 382). The RTID group showed lower overall TDI rate (18.6% vs. 40.5%; P < 0.001) and unnecessary stoma formation (17.7% vs. 41.3%; P < 0.001). Although a numerical increase in necessary TDI use was observed (55.6% vs. 10.0%), this difference was not significant (P = 0.057). Critically, the incidence of the co-primary safety outcome, AL, was comparable between the RTID and control groups (2.4% vs. 2.7%; P = 0.753), indicating that the pre-specified TDI reduction endpoint was met, while the study was underpowered to formally test non-inferiority for AL. RTID offers an objective tool to support TDI decision-making without a compromise in safety. The present study was registered on ClinicalTrials.gov (no. NCT04999007) on August 8, 2021.

Indexed as

IleostomyMachine LearningRectal NeoplasmsAgedAnastomotic LeakColorectal Surgical ProceduresFemaleHumansMaleMiddle AgedPredictive Learning Models

Identifiers

PMID42185305
PMCPMC13385896

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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.