Evidence map›Paper›PMID 36497730›Full record

Trial reportInternational journal of environmental research and public health2022

Development and Validation of a Nomogram Based on Metabolic Risk Score for Assessing Lymphovascular Space Invasion in Patients with Endometrial Cancer.

Jingyuan Wang, Xingchen Li, Xiao Yang, Jianliu Wang

Open access · goldAbstract readRandomized Controlled Trial
In one paragraph

Trial report in International journal of environmental research and public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
1.3field-weighted citation impact, top 19% 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

7 citing papers in PubMed, 8 citations in OpenAlex.

  1. Trial
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. 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 at 1 institution in 1 country.

Jingyuan WangDepartment of Obstetrics and Gynecology, Peking University People's Hospital, Beijing 100044, China.
Xingchen LiDepartment of Obstetrics and Gynecology, Peking University People's Hospital, Beijing 100044, China.
Xiao YangDepartment of Obstetrics and Gynecology, Peking University People's Hospital, Beijing 100044, China.
Jianliu WangDepartment of Obstetrics and Gynecology, Peking University People's Hospital, Beijing 100044, China.
Peking University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveThis study assessed the predictive value of the metabolic risk score (MRS) for lymphovascular space invasion (LVSI) in endometrial cancer (EC) patients.

methodsWe included 1076 patients who were diagnosed with EC between January 2006 and December 2020 in Peking University People's Hospital. All patients were randomly divided into the training and validation cohorts in a ratio of 2:1. Data on clinicopathological indicators were collected. Univariable and multivariable logistic regression analysis was used to define candidate factors for LVSI. A backward stepwise selection was then used to select variables for inclusion in a nomogram. The performance of the nomogram was evaluated by discrimination, calibration, and clinical usefulness.

resultsIndependent predictors of LVSI included differentiation grades (G2: OR = 1.800, 95% CI: 1.050-3.070,

conclusionMRS-based nomograms are useful for predicting LVSI in patients with EC and may facilitate better clinical decision-making.

Indexed as

Endometrial NeoplasmsNomogramsFemaleHumansRetrospective StudiesRisk FactorsROC Curveendometrial cancerlymphovascular space invasionmetabolismnomogram

Identifiers

PMID36497730
PMCPMC9736227
OpenAlexW4309936113

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.