Evidence map›Paper›PMID 35368922›Full record

ArticleJournal of healthcare engineering2022

Evaluation of the Effect of Comprehensive Nursing Interventions on Plaque Control in Patients with Periodontal Disease in the Context of Artificial Intelligence.

Juan Xu, Lingling Wang, Hongxia Sun, Shanshan Liu

RetractedOpen access · hybridAbstract readRetracted Publication
In one paragraph

Article in Journal of healthcare engineering, 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 It has been retracted, and should not be counted.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 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
2.3field-weighted citation impact, top 12% 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

6 citing papers in PubMed, 15 citations in OpenAlex.

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  4. Review
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  6. Review
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

4 authors at 2 institutions in 1 country.

Juan XuDepartment of Stomatology, First People's Hospital of Yongkang City, Yongkang City, Zhejiang Province, China.
Lingling WangDepartment of Internal Medicine-Cardiovascular Department Xiangyang No. 1 People'sHospital, Hubei University of Medicine, Xiangyang 441000, China.
Hongxia SunQingdao Jimo District Tongji Health Center and Medical Nursing, Qingdao, Shandong 266228, China.
Shanshan LiuDepartment of Stomatology, Fourth Affiliated Hospital, Hebei Medical University, Shijiazhuang 050017, Hebei, China.ORCID 0000-0002-9756-689X
Hebei Medical University · CNHubei University of Medicine · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Plaque is a bacterial biofilm that adheres to each other and exists on the tooth surface, and new plaque can continuously reform after removing it from the tooth surface. The pathogenesis of periodontal disease is related to the bacteria, the host and the environment, with the bacteria and bacterial products in plaque being the main initiators of periodontal disease. The effective control of plaque is an effective method for the treatment and prevention of periodontal disease and is often underappreciated in clinical practice. For the traditional diagnostic method through experience and visual observation, it may lead to misdiagnosis and underdiagnosis. In order to accurately diagnose plaque disease, this study designed a convolutional neural network-based oral dental disease diagnosis system for oral care interventions to improve oral health awareness. Thus motivate patients to implement proper oral health care measures, and continuously and lifelong insist on thorough daily plaque removal to improve patients' physical health and quality of life in periodontal disease patients.

Indexed as

Artificial IntelligencePeriodontal DiseasesBacteriaBiofilmsHumansQuality of Life

Identifiers

PMID35368922
PMCPMC8967516
OpenAlexW4226002961

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.