Evidence map›Paper›PMID 37920574›Full record

ArticleFrontiers in public health2023

Exploring transfer learning in chest radiographic images within the interplay between COVID-19 and diabetes.

Muhammad Shoaib, Nasir Sayed, Babar Shah, Tariq Hussain, Ahmad Ali AlZubi, Sufian Ahmad AlZubi, Farman Ali

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in public health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
0.5field-weighted citation impact, top 32% 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, 2 citations in OpenAlex.

No citing paper in PubMed yet.

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

7 authors at 7 institutions in 6 countries.

Muhammad ShoaibDepartment of Computer Science, CECOS University of IT and Emerging Sciences, Peshawar, Pakistan.
Nasir SayedDepartment of Computer Science, Islamia College Peshawar, Peshawar, Pakistan.
Babar ShahCollege of Technological Innovation, Zayed University, Dubai, United Arab Emirates.
Tariq HussainSchool of Computer Science and Technology, Zhejiang Gongshang University, Hangzhou, China.
Ahmad Ali AlZubiDepartment of Computer Science, Community College, King Saud University, Riyadh, Saudi Arabia.
Sufian Ahmad AlZubiFaculty of Medicine, Jordan University of Science and Technology, Irbid, Jordan.
Farman AliDepartment of Computer Science and Engineering, School of Convergence, College of Computing and Informatics, Sungkyunkwan University, Seoul, Republic of Korea.
CECOS University · PKIslamia College University · PKJordan University of Science and Technology · JOKing Saud University · SASungkyunkwan University · KRZayed University · AEZhejiang Gongshang University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The intricate relationship between COVID-19 and diabetes has garnered increasing attention within the medical community. Emerging evidence suggests that individuals with diabetes may experience heightened vulnerability to COVID-19 and, in some cases, develop diabetes as a post-complication following the viral infection. Additionally, it has been observed that patients taking cough medicine containing steroids may face an elevated risk of developing diabetes, further underscoring the complex interplay between these health factors. Based on previous research, we implemented deep-learning models to diagnose the infection

Indexed as

COVID-19Diabetes MellitusHumansLearningMachine LearningRadiographyCOVID-19 diseasediabetesdiagnosis using deep learningdisease detectiontransfer learning

Identifiers

PMID37920574
PMCPMC10619728
OpenAlexW4387742057

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.