Evidence map›Paper›PMID 37038751›Full record

Trial reportRenal failure2023

A preliminary nomogram model for predicting relapse of patients with primary membranous nephropathy.

Min Li, Huifang Wang, Xiaoying Lai, Dandan Guo, Chunhui Jiang, Zixuan Fu, Xuemei Liu

Open access · goldAbstract readRandomized Controlled Trial
In one paragraph

Trial report in Renal failure, 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
1.2field-weighted citation impact, top 22% 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

5 citing papers in PubMed, 6 citations in OpenAlex.

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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

7 authors at 2 institutions in 1 country.

Min LiDepartment of Nephrology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, P. R. China.
Huifang WangDepartment of Nephrology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, P. R. China.
Xiaoying LaiDepartment of Nephrology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, P. R. China.
Dandan GuoDepartment of Nephrology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, P. R. China.
Chunhui JiangDepartment of Nephrology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, P. R. China.
Zixuan FuDepartment of Nephrology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, P. R. China.
Xuemei LiuDepartment of Nephrology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, P. R. China.
Qingdao University · CNAffiliated Hospital of Qingdao University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo explore the predictive factors and establish a nomogram model for predicting relapse risk in primary membranous nephropathy (PMN).

methodsThe clinical, laboratory, pathological and follow-up data of patients with biopsy-proven membranous nephropathy were collected in the Affiliated Hospital of Qingdao University. A total of 400 PMN patients who achieved remission were assigned to the development group (

resultHyperuricemia (HR = 2.938, 95% CI 1.875-4.605,

conclusionsHyperuricemia, remission status, CRP and therapeutic regimen were predictive factors for relapse of PMN. A novel nomogram model with good discrimination and calibration was constructed to predict relapse risk in patients with PMN early.

Indexed as

Calcineurin InhibitorsC-Reactive ProteinGlomerulonephritis, MembranousHyperuricemiaChronic DiseaseHumansNomogramsRecurrenceRetrospective StudiesCalcineurin InhibitorsC-Reactive Proteinnomogram modelpredictive factorsPrimary membranous nephropathyrelapse

Identifiers

PMID37038751
PMCPMC10101672
OpenAlexW4364352224

What Socratic holds

Textmetadata
LicenceCC BY-NC
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 Socratic graph.