Evidence map›Paper›PMID 40926185›Full record

ArticleRenal failure2025

Predicting kidney replacement therapy, cardiovascular disease and all-cause mortality in advanced chronic kidney disease among the Chinese population.

Xingchen Yao, Chenglong Li, Jinwei Wang, Lanlan Lu, Chao Yang, Luxia Zhang

Abstract read
In one paragraph

Article in Renal failure, 2025. 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
–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

0 citing papers in PubMed.

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

6 authors.

Xingchen YaoRenal Division, Department of Medicine, Peking University First Hospital, Peking University Institute of Nephrology, Beijing, China.
Chenglong LiNational Institute of Health Data Science at Peking University, Peking University Health Science Center, Beijing, China.
Jinwei WangRenal Division, Department of Medicine, Peking University First Hospital, Peking University Institute of Nephrology, Beijing, China.
Lanlan LuXiaying Primary Health Care Center, Ningbo, China.
Chao YangRenal Division, Department of Medicine, Peking University First Hospital, Peking University Institute of Nephrology, Beijing, China.
Luxia ZhangRenal Division, Department of Medicine, Peking University First Hospital, Peking University Institute of Nephrology, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The Grams model, designed to predict adverse event risks in advanced chronic kidney disease (CKD) patients, was evaluated in a Chinese cohort of 1,333 patients with eGFR below 30 mL/min/1.73 m

Indexed as

Cardiovascular DiseasesRenal Insufficiency, ChronicRenal Replacement TherapyAgedChinaEast Asian PeopleFemaleGlomerular Filtration RateHumansMaleMiddle AgedPrognosisRisk AssessmentRisk FactorsAdvanced chronic kidney diseasecardiovascular diseaseexternal validationkidney replacement therapy

Identifiers

PMID40926185
PMCPMC12422041

What OpenQuestion holds

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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.