Evidence mapPaperPMID 34374903Full record

ReviewClinical and experimental nephrology2022

Clinical epidemiological analysis of cohort studies investigating the pathogenesis of kidney disease.

Shigeru Tanaka, Toshiaki Nakano, Kazuhiko Tsuruya, Takanari Kitazono

Abstract readReview
In one paragraph

Review in Clinical and experimental nephrology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

4 authors.

Shigeru TanakaDepartment of Medicine and Clinical Science, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka, 812-8582, Japan. tanaka.shigeru.170@m.kyushu-u.ac.jp.ORCID http://orcid.org/0000-0002-8916-142X
Toshiaki NakanoDepartment of Medicine and Clinical Science, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka, 812-8582, Japan.
Kazuhiko TsuruyaDepartment of Nephrology, Nara Medical University, Nara, Japan.
Takanari KitazonoDepartment of Medicine and Clinical Science, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka, 812-8582, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In recent years, large cohort studies of patients with chronic kidney disease (CKD) have been established all over the world. These studies have attempted to analyze the pathogenesis of CKD using a large body of published evidence. The design of cohort studies is characterized by the measurement of the exposure prior to the occurrence of the outcome, which has the advantage of clarifying the temporal relationship between predictors and outcomes and estimating the strength of the causal relationship between predictors and multiple outcomes. Recent advances in biostatistical analysis methods, such as propensity scores and risk prediction models, are facilitating causal inference using higher quality evidence with greater precision in observational studies. In this review, we will discuss clinical epidemiological research of kidney disease based on the analysis of observational cohort data sets, with a focus on our previous studies.

Indexed as

Renal Insufficiency, ChronicCohort StudiesHumansCausal effectEpidemiologyObservational study

Identifiers

PMID34374903
PMCPMC8738501

What Socratic 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 Socratic graph.