Evidence map›Paper›PMID 33411820›Full record

ArticlePloS one2021

The value of TI-RADS combined with superb microvascular imaging in distinguishing thyroid nodules: A protocol for systematic review and meta-analysis.

Cong Wang, Mingxin Lin, Lin Zhong, Congliang Tian

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 1 pooled it
0.4field-weighted citation impact, top 37% 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

3 citing papers in PubMed, 1 synthesis or guideline pooled it, 3 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

4 authors at 1 institution in 1 country.

Cong WangUltrasound Department of the First Affiliated Hospital of Dalian Medical University, Dalian, China.
Mingxin LinUltrasound Department of the First Affiliated Hospital of Dalian Medical University, Dalian, China.
Lin ZhongPathology Department of the First Affiliated Hospital of Dalian Medical University, Dalian, China.
Congliang TianPediatrics Department of the First Affiliated Hospital of Dalian Medical University, Dalian, China.ORCID 0000-0002-7189-2267
Dalian Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAs a novel ultrasonic technique, superb microvascular imaging (SMI) can quickly, simply and noninvasively observe the microvascular distribution in a tumor and evaluate the microvascular perfusion. Previous studies have shown that SMI can detect the blood flow signals of neovascularization in tumors and increase the sensitivity for detecting thyroid cancer. However, the results of these studies have been contradictory, and the sample sizes were too small. Therefore, the present meta-analysis will aim at evaluating the value of a thyroid imaging report and data system (TI-RADS) combined with SMI in distinguishing between benign and malignant thyroid nodules.

methodsWe will search PubMed, Web of Science, Cochrane Library, Google Scholar, and Chinese biomedical databases from their inceptions to the June 31, 2020. Two authors will independently carry out searching literature records; scanning titles, abstracts, and full texts; collecting data; and assessing risk of bias. Review Manager 5.2 and Stata14.0 software will be used for data analysis.

resultsThis systematic review will evaluate the value of TI-RADS combined with SMI in distinguishing between benign and malignant thyroid nodules. SYSTEMATIC REVIEW REGISTRATION: INPLASY202070113.

Indexed as

Meta-Analysis as TopicSystematic Reviews as TopicHumansThyroid NoduleUltrasonography

Identifiers

PMID33411820
PMCPMC7790367
OpenAlexW3043858723

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.