Evidence map›Paper›PMID 36262984›Full record

ArticleContrast media & molecular imaging2022

Intelligent Algorithm-Based Ultrasound Images in Evaluation of Therapeutic Effects of Radiofrequency Ablation for Liver Tumor and Analysis on Risk Factors of Postoperative Infection.

Lou Kexin, Chen Ning, Li Zhihong, Xiao Shuo, Wu Rong

RetractedOpen access · hybridAbstract readRetracted Publication
In one paragraph

Article in Contrast media & molecular imaging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. 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, top 90% of its field
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, 0 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

5 authors at 3 institutions in 1 country.

Lou KexinDepartment of Medical Ultrasound, Shanghai General Hospital of Nanjing Medical University, Shanghai 201600, China.
Chen NingGraduate School, Xuzhou Medical University, Xuzhou 221004, Jiangsu, China.
Li ZhihongDepartment of Medical Ultrasound, Shanghai General Hospital of Nanjing Medical University, Shanghai 201600, China.
Xiao ShuoSchool of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221000, Jiangsu, China.
Wu RongDepartment of Medical Ultrasound, Shanghai General Hospital of Nanjing Medical University, Shanghai 201600, China.ORCID 0000-0002-9386-462X
Shanghai Medical College of Fudan University · CNChina University of Mining and Technology · CNXuzhou Medical College · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This research aimed to explore the therapeutic effects of radiofrequency ablation (RFA) for liver tumors and to investigate the postoperative infection factors. Specifically, 80 patients with liver tumors undergoing ultrasound-guided FRA were selected as research subjects. They were diagnosed in the hospital. An intelligent fitting (IF) algorithm was compared with a genetic algorithm (GA) and applied to the RFA of the 80 patients. It was found that the running time of the IF algorithm was about 0.2 times than that of the GA, demonstrating better global searching capabilities. The mean diameter of single liver tumors was (3.45 ± 1.24) cm, and the complete ablation rate of tumors with diameters less than 3 cm was 87.88%, that of tumors with diameters of 3-5 cm was 72.92%, and that of tumors with a diameter of more than 5 cm was 63.33%. Posttreatment, the AST level decreased significantly and the ALB level increased significantly, and the difference was notable (

Indexed as

Catheter AblationLiver NeoplasmsRadiofrequency AblationAlgorithmsHumansRisk FactorsTreatment Outcome

Identifiers

PMID36262984
PMCPMC9546717
OpenAlexW4298091682

What OpenQuestion holds

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LicenceCC BY
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.