Evidence map›Paper›PMID 37240872›Full record

ArticleJournal of personalized medicine2023

Kidney Health and Care: Current Status, Challenges, and Developments.

Ming-Yen Lin, Yi-Wen Chiu, Yu-Hsuan Lin, Yihuang Kang, Ping-Hsun Wu, Jeng-Huei Chen, Hsing Luh, Shang-Jyh Hwang, On Behalf Of The iH Research Group

Abstract read
In one paragraph

Article in Journal of personalized medicine, 2023. 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
–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

2 citing papers in PubMed.

  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

9 authors.

Ming-Yen LinDivision of Nephrology, Department of Internal Medicine, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung 807, Taiwan.ORCID 0000-0002-8194-4833
Yi-Wen ChiuDivision of Nephrology, Department of Internal Medicine, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung 807, Taiwan.
Yu-Hsuan LinTaiwan Instrument Research Institute, National Applied Research Laboratories, Hsinchu 300, Taiwan.
Yihuang KangDepartment of Information and Management, National Sun Yat-Sen University, Kaohsiung 804, Taiwan.ORCID 0000-0003-4431-7977
Ping-Hsun WuDivision of Nephrology, Department of Internal Medicine, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung 807, Taiwan.ORCID 0000-0003-1021-8161
Jeng-Huei ChenDepartment of Mathematical Sciences, National Chengchi University, Taipei 116, Taiwan.
Hsing LuhDepartment of Mathematical Sciences, National Chengchi University, Taipei 116, Taiwan.ORCID 0000-0002-7570-0584
Shang-Jyh HwangDivision of Nephrology, Department of Internal Medicine, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung 807, Taiwan.ORCID 0000-0002-9404-3305
On Behalf Of The iH Research Group

Funding

Taiwan Ministry of Science and Technology MOST-108-2314-B-037-110Taiwan National Health Research Institutes NHRI-EX112-11208PI, NHRI-EX109-10717PI and NHRI-EX108-10505PI
6 · The paper itself

Abstract

The concept of chronic kidney disease (CKD) originated in the 2000s, and an estimated 850 million patients are currently suffering from health threats from different degrees of CKD. However, it is unclear whether the existing CKD care systems are optimal for improving patient prognosis and outcomes, so this review summarizes the burden, existing care models, effectiveness, challenges, and developments of CKD care. Even under the general care principles, there are still significant gaps in our understanding of the causes of CKD, prevention or care resources, and care burdens between countries worldwide. Receiving care from multidisciplinary teams rather than only a nephrologist shows potential profits in comprehensive and preferable outcomes. In addition, we propose a novel CKD care structure that combines modern technologies, biosensors, longitudinal data visualization, machine learning algorithms, and mobile care. The novel care structure could simultaneously change the care process, significantly reduce human contact, and make the vulnerable population less likely to be exposed to infectious diseases such as COVID-19. The information offered should be beneficial, allowing us to rethink future CKD care models and applications to reach the goals of health equality and sustainability.

Indexed as

digital healthhealthcare systemhealth equitykidney caresustainability

Identifiers

PMID37240872
PMCPMC10219474

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.