Evidence map›Paper›PMID 34336150›Full record

ArticleJournal of healthcare engineering2021

Image Enhancement Technology Based on Deep Trust Network Model in Clinical Treatment of Traumatology and Orthopedics.

Qiaomu He, Shenghao Chen, Lei Li

Open access · hybridAbstract read
In one paragraph

Article in Journal of healthcare engineering, 2021. 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
1.1field-weighted citation impact, top 22% 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

2 citing papers in PubMed, 5 citations in OpenAlex.

  1. Article
  2. Article
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

3 authors at 1 institution in 1 country.

Qiaomu HeSuizhou Hospital, Hubei University of Medicine, Department of Orthopedics, Hubei, Suizhou 441300, China.
Shenghao ChenSuizhou Hospital, Hubei University of Medicine, Department of Orthopedics, Hubei, Suizhou 441300, China.
Lei LiSuizhou Hospital, Hubei University of Medicine, Department of Orthopedics, Hubei, Suizhou 441300, China.ORCID 0000-0003-3266-0604
Hubei University of Medicine · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Fractures have brought great pain to patients, and treatment requires a lot of time and yield slow results, which seriously affect the production and life of people. Fractures are mostly treated with traditional conservative treatment methods. For orthopedic trauma, image enhancement technology has gradually played an important role in the clinical treatment of orthopedic trauma and has become a kind of suffering. It has become a new treatment method that attracts people's attention. In order to study the application of image enhancement technology based on the deep trust network model in the clinical treatment of trauma and orthopedics, this paper conducted a related survey of fracture patients in the city's first hospital, reviewed relevant literature, and interviewed professionals, and we collected relevant material, constructed case templates, and created clinical research models using comprehensive quantitative and qualitative analytical techniques. Studies have shown that the use of image enhancement techniques in the treatment of fractures has been successful, with healing efficiency approximately 20% faster than conservative treatment. In the clinical treatment of trauma and orthopedics, image enhancement technology can effectively reduce the incidence of complications in the prognosis of patients.

Indexed as

Biomedical EnhancementOrthopedicsTraumatologyHumansImage Enhancement

Identifiers

PMID34336150
PMCPMC8289591
OpenAlexW3182812030

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.