Evidence map›Paper›PMID 37332881›Full record

ArticleCanadian respiratory journal2023

Predictive Factors of Nonmalignant Pathological Diagnosis and Final Diagnosis of Ultrasound-Guided Cutting Biopsy for Peripheral Pulmonary Diseases.

Qing Li, Li Zhang, Xinhong Liao, Yanfen Zhong, Zhixian Li

Open access · goldAbstract read
In one paragraph

Article in Canadian respiratory journal, 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
0.3field-weighted citation impact, top 38% 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, 1 citations in OpenAlex.

  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

5 authors at 1 institution in 1 country.

Qing LiDepartment of Diagnostic Ultrasound, The Affiliated Hospital of Youjiang Medical University for Nationalities, Baise 533000, Guangxi, China.
Li ZhangDepartment of Diagnostic Ultrasound, First Affiliated Hospital of Guangxi Medical University, Nanning 530021, Guangxi, China.
Xinhong LiaoDepartment of Diagnostic Ultrasound, First Affiliated Hospital of Guangxi Medical University, Nanning 530021, Guangxi, China.
Yanfen ZhongDepartment of Diagnostic Ultrasound, First Affiliated Hospital of Guangxi Medical University, Nanning 530021, Guangxi, China.
Zhixian LiDepartment of Diagnostic Ultrasound, First Affiliated Hospital of Guangxi Medical University, Nanning 530021, Guangxi, China.ORCID 0000-0002-7469-5349
Guangxi Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study aimed to explore the predictive factors of nonmalignant pathological diagnosis and final diagnosis of ultrasound-guided cutting biopsy for peripheral pulmonary diseases. A total of 470 patients with peripheral lung disease diagnosed as nonmalignant by ultrasound-guided cutting biopsy in the First Affiliated Hospital of Guangxi Medical University from January 2017 to May 2020 were included. Ultrasound biopsy was performed to determine the correctness of pathological diagnosis. Independent risk factors of malignant tumor were predicted by multivariate logistic regression analysis. Pathological biopsy results showed that 162 (34.47%) of the 470 biopsy data were specifically benign, and 308 (65.53%; malignant lesions: 25.3%, benign lesions: 74.7%) were nondiagnostic findings. The final diagnoses were benign in 387 cases and malignant in 83 cases. In the nondiagnostic biopsy malignant risk prediction analysis, lesion size (OR = 1.025,

Indexed as

Lung DiseasesTomography, X-Ray ComputedChinaHumansImage-Guided BiopsyRetrospective StudiesUltrasonography, Interventional

Identifiers

PMID37332881
PMCPMC10275682
OpenAlexW4380087835

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.