Evidence map›Paper›PMID 40837766›Full record

ArticleWorld journal of gastrointestinal oncology2025

Predictive model for early postoperative stomal complications in colorectal cancer: A systematic review.

Payal Kaw, Ashok Kumar

Abstract read
In one paragraph

Article in World journal of gastrointestinal oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Payal KawDepartment of Surgical Gastroenterology, Sanjay Gandhi Post Graduate Institute of Medical Sciences, Lucknow 226014, Uttar Pradesh, India.
Ashok KumarDepartment of Surgical Gastroenterology, Sanjay Gandhi Post Graduate Institute of Medical Sciences, Lucknow 226014, Uttar Pradesh, India. doc.ashokgupta@gmail.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundStomal complications though small in early postoperative period, but poses significant morbidity, therapeutic challenge, delay in adjuvant treatment and sometimes even leads to mortality. Predictive model for early detection of stomal complications is important to improve the outcome. A model including patients and disease related factors, intraoperative surgical techniques and biochemical markers would be a better determinant to anticipate early stomal complications. Incorporation of emerging tools and technology such as artificial intelligence (AI), will further improve the prediction.

aimTo identify various risk factors and models for prediction of early post operative stomal complications in colorectal cancer (CRC) surgery.

methodsPublished literatures on early postoperative stomal complications in CRC surgery were systematically reviewed between 1995 and 2024 from online search engines PubMed and MEDLINE.

resultsTwenty-four observational studies focused on identifying various risk factors for early post operative stomal complications in CRC surgery were analyzed. Stomal complications in CRC are influenced by several factors such as disease factors, patient-specific characteristics, and surgical techniques. There are some biomarkers and tools loke AI which may play significant roles in early detection.

conclusionCareful analysis of these factors, changes in biochemical parameters, and application of AI, a predictive model for stomal complications can be generated, to help in early detection, prompt action to achieve better outcomes.

Indexed as

Artificial intelligenceBiochemical markersColorectal cancerDisease factorsPatient’ factorsPredictive modelStomal complicationsSurgeon factor

Identifiers

PMID40837766
PMCPMC12362536

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

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.