Evidence map›Paper›PMID 35198037›Full record

ArticleComputational and mathematical methods in medicine2022

Analysis of the Curative Effect of Continuous Nursing Based on Data Mining on Patients with Liver Tumors.

ChunFen Peng, LiHong Bao, JingQiong Wang

RetractedOpen access · hybridAbstract readComparative StudyRetracted Publication
In one paragraph

Article in Computational and mathematical methods in medicine, 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 It has been retracted, and should not be counted. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.3field-weighted citation impact, top 33% 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

1 citing paper in PubMed, 1 citations in OpenAlex.

  1. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

3 authors at 1 institution in 1 country.

ChunFen PengCancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
LiHong BaoCancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
JingQiong WangCancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.ORCID https://orcid.org/0000-0002-0981-1809
Union Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Studies have shown that the physical, psychological, and social problems of liver cancer patients are more serious than those of other cancer patients and their quality of life is significantly reduced. This may be related to the poor treatment effect of patients with advanced liver cancer. Patients often have adverse symptoms such as cancer pain, pleural effusion, and ascites, etc., which have a great impact on patients' psychology and recovery from illness. With the change of the medical model, it has become history to rely solely on drugs to care for patients with advanced liver cancer and comprehensive nursing intervention has become very important. Continuous nursing intervention focuses on individualized and full-hearted care, effectively alleviating patients' anxiety and fear and improving patients' environmental adaptability and psychological defense mechanisms. However, in the field of liver cancer, there is no detailed comparison between the efficacy of continuous nursing and traditional conventional nursing. This article applies the hidden Markov model, starts with medical data mining, and describes the process achieved by the application of this article and the analysis of the results obtained by the two nursing methods, which reflect the difference in curative effect evaluation, and it proves that continuous nursing has more advantages in the curative effect of patients with liver tumors.

Indexed as

Models, NursingAlgorithmsChinaComputational BiologyData MiningHumansLiver NeoplasmsMarkov Chains

Identifiers

PMID35198037
PMCPMC8860545
OpenAlexW4212901052

What OpenQuestion holds

Textmetadata
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.