Evidence map›Paper›PMID 38132374›Full record

ArticleCurrent oncology (Toronto, Ont.)2023

Identifying Optimal Candidates for Trimodality Therapy among Nonmetastatic Muscle-Invasive Bladder Cancer Patients.

Shengming Ran, Jingtian Yang, Jintao Hu, Liekui Fang, Wang He

Open access · goldAbstract read
In one paragraph

Article in Current oncology (Toronto, Ont.), 2023. 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
0.6field-weighted citation impact, top 28% 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

1 citing paper in PubMed, 2 citations in OpenAlex.

  1. Review
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

5 authors at 3 institutions in 1 country.

Shengming RanDepartment of Urology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou 510289, China.
Jingtian YangDepartment of Urology, The Third People's Hospital of Shenzhen, Southern University of Science and Technology, Shenzhen 518116, China.
Jintao HuDepartment of Urology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou 510289, China.
Liekui FangDepartment of Urology, The Third People's Hospital of Shenzhen, Southern University of Science and Technology, Shenzhen 518116, China.
Wang HeDepartment of Urology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou 510289, China.
Southern University of Science and Technology · CNSun Yat-sen Memorial Hospital · CNSun Yat-sen University · CN

Funding

National Natural Science Foundation of China 81825016National Natural Science Foundation of China 81972385National Natural Science Foundation of China 82173266
6 · The paper itself

Abstract

(1) Background: This research aims to identify candidates for trimodality therapy (TMT) or radical cystectomy (RC) by using a predictive model. (2) Methods: Patients with nonmetastatic muscle-invasive bladder cancer (MIBC) in the Surveillance, Epidemiology, and End Results (SEER) database were enrolled. The clinical data of 2174 eligible patients were extracted and separated into RC and TMT groups. To control for confounding bias, propensity score matching (PSM) was carried out. A nomogram was established via multivariable logistic regression. The area under the receiver operating characteristic curve (AUC) and calibration curves were used to assess the nomogram's prediction capacity. Decision curve analysis (DCA) was carried out to determine the nomogram's clinical applicability. (3) Results: After being processed with PSM, the OS of the RC group was significantly longer compared with the TMT group (

Indexed as

Urinary Bladder NeoplasmsCystectomyHumansMusclesNeoadjuvant TherapyNomogramsnomogramnonmetastatic muscle-invasive bladder cancerpredictive modelSEER databasetrimodality therapy

Identifiers

PMID38132374
PMCPMC10742539
OpenAlexW4389143002

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.