Evidence map›Paper›PMID 37213169›Full record

ArticleJournal of medical Internet research2023

Contribution of the 5G Smart First-Aid Care Platform to Achieving High-Quality Prehospital Care.

Tao Xiang, Pei Yong Zhang, Guang Ying Zhuo, Hang Dai

Abstract read
In one paragraph

Article in Journal of medical Internet research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
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

5 citing papers in PubMed.

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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

4 authors.

Tao Xiang *Department of Emergency, The Third People's Hospital of Chengdu, Chengdu, Sichuan, China.ORCID 0000-0002-5882-6521
Pei Yong Zhang *Department of Emergency, The Third People's Hospital of Chengdu, Chengdu, Sichuan, China.ORCID 0000-0001-9579-8438
Guang Ying Zhuo *Department of Emergency, The Third People's Hospital of Chengdu, Chengdu, Sichuan, China.ORCID 0000-0001-8917-1305
Hang Dai *Department of Emergency, The Third People's Hospital of Chengdu, Chengdu, Sichuan, China.ORCID 0000-0003-2968-6467

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

China is gradually becoming an aging society, and the necessity for prehospital first-aid care is increasing. However, there is a long-term information blind spot in traditional prehospital first-aid care. Fifth-generation (5G) network has the advantages of enhanced broadband, multiple connections, and low latency. Combined with the current prehospital first-aid system, the 5G smart medical prehospital first-aid care model creates a new opportunity for the development of prehospital first-aid care. This paper aimed to describe the 5G smart first-aid care platform and offers practical insights into the construction and application of the 5G smart first-aid care platform in small- and medium-sized cities. We first introduced the working principle of the 5G smart first-aid care platform and then chose patients with prehospital chest pain as an example to describe the whole workflow in detail. The application of the 5G smart emergency-care platform is at the stage of pilot exploration in large- and medium-sized cities. Big data statistical analysis of the completed first-aid care tasks has not been performed yet. The 5G smart first-aid care platform realizes real-time interconnection of information between the ambulance and the hospital, performs remote consultation, shortens the treatment time, and enhances treatment efficiency. Future research should focus on quality control analysis of the 5G smart first-aid care platform.

Indexed as

Emergency Medical ServicesRemote ConsultationAmbulancesChinaHospitalsHumans5Gfifth generationprehospital first-aid caresmart medical caretelemedicine

Identifiers

PMID37213169
PMCPMC10242467

What OpenQuestion holds

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Registered trials

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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.