Evidence map›Paper›PMID 34697567›Full record

ArticleJournal of healthcare engineering2021

Computed Tomography Image Texture under Feature Extraction Algorithm in the Diagnosis of Effect of Specific Nursing Intervention on Mycoplasma Pneumonia in Children.

Yuyan Bi, Cuifeng Jiang, Hua Qi, Haiwei Zhou, Lixia Sun

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 1 paper, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed, 1 pooled it
0.1field-weighted citation impact, top 50% 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 synthesis or guideline pooled it, 1 citations in OpenAlex.

  1. Pooled it
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

5 authors at 1 institution in 1 country.

Yuyan BiDepartment of Pediatric Ward, Jinan City People's Hospital, Jinan 271199, Shandong Province, China.ORCID 0000-0001-6147-1103
Cuifeng JiangDepartment of Pediatric Surgery, Jinan City People's Hospital, Jinan 271199, Shandong Province, China.ORCID 0000-0002-3945-1680
Hua QiDepartment of Pediatric Ward, Jinan City People's Hospital, Jinan 271199, Shandong Province, China.ORCID 0000-0002-6409-4187
Haiwei ZhouDepartment of Pediatric Ward, Jinan City People's Hospital, Jinan 271199, Shandong Province, China.ORCID 0000-0002-3914-7662
Lixia SunDepartment of Nursing, Jinan City People's Hospital, Jinan 271199, Shandong Province, China.ORCID 0000-0001-6376-7434
Jinan City People's Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

To evaluate the effect of specific nursing intervention in children with mycoplasma pneumonia (MP), a feature extraction algorithm based on gray level co-occurrence matrix (GLCM) was proposed and combined with computed tomography (CT) image texture features. Then, 98 children with MP were rolled into the observation group with 49 cases (specific nursing) and the control group with 49 cases (routine nursing). CT images based on feature extraction algorithm of optimized GLCM were used to examine the children before and after nursing intervention, and the recovery of the two groups of children was discussed. The results showed that the proportion of lung texture increase, rope shadow, ground glass shadow, atelectasis, and pleural effusion in the observation group (24.11%, 3.86%, 8.53%, 15.03%, and 3.74%) was significantly lower than that in the control group (28.53%, 10.23%, 13.34%, 21.15%, and 8.13%) after nursing (

Indexed as

Pleural EffusionPneumonia, MycoplasmaAlgorithmsChildHumansLungTomography, X-Ray Computed

Identifiers

PMID34697567
PMCPMC8541873
OpenAlexW3205212296

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.