Evidence map›Paper›PMID 39001373›Full record

ArticleCancers2024

Scoring System for Predicting the Risk of Liver Cancer among Diabetes Patients: A Random Survival Forest-Guided Approach.

Sarah Tsz-Yui Yau, Eman Yee-Man Leung, Chi-Tim Hung, Martin Chi-Sang Wong, Ka-Chun Chong, Albert Lee, Eng-Kiong Yeoh

Abstract read
In one paragraph

Article in Cancers, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

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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

2 citing papers in PubMed.

  1. Article
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4 · The record

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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

7 authors.

Sarah Tsz-Yui YauJC School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong, China.
Eman Yee-Man LeungJC School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong, China.
Chi-Tim HungJC School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong, China.ORCID 0000-0003-2103-8377
Martin Chi-Sang WongJC School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong, China.ORCID 0000-0001-7706-9370
Ka-Chun ChongJC School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong, China.ORCID 0000-0001-5610-1298
Albert LeeJC School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong, China.ORCID 0000-0002-5888-5660
Eng-Kiong YeohJC School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong, China.

Funding

Government of Hong Kong S2019.A4.015.19S
6 · The paper itself

Abstract

backgroundMost liver cancer scoring systems focus on patients with preexisting liver diseases such as chronic viral hepatitis or liver cirrhosis. Patients with diabetes are at higher risk of developing liver cancer than the general population. However, liver cancer scoring systems for patients in the absence of liver diseases or those with diabetes remain rare. This study aims to develop a risk scoring system for liver cancer prediction among diabetes patients and a sub-model among diabetes patients without cirrhosis/chronic viral hepatitis.

methodsA retrospective cohort study was performed using electronic health records of Hong Kong. Patients who received diabetes care in general outpatient clinics between 2010 and 2019 without cancer history were included and followed up until December 2019. The outcome was diagnosis of liver cancer during follow-up. A risk scoring system was developed by applying random survival forest in variable selection, and Cox regression in weight assignment.

resultsThe liver cancer incidence was 0.92 per 1000 person-years. Patients who developed liver cancer (

conclusionsThe proposed scoring system may provide a parsimonious score for liver cancer risk prediction among diabetes patients.

Indexed as

diabetesliver cancerrandom forestrisk predictionsurvival data

Identifiers

PMID39001373
PMCPMC11240698

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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.