Evidence map›Paper›PMID 28078296›Full record

ArticleBioMed research international2016

The Value of the Model and Quantitative Parameters of Contrast-Enhanced Ultrasound in Judging the Severity of SHPT.

Xing-Xin Liang, Fan Li, Feng Gao, Yang Liu, Xiao-Hui Qiao, Zheng Zhang, Lian-Fang Du

Abstract read
In one paragraph

Article in BioMed research international, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
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. Observational
  2. Review
  3. 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

7 authors.

Xing-Xin LiangDepartment of Medical Ultrasound, Jiangwan Hospital, Hongkou District, Shanghai, China.
Fan LiDepartment of Medical Ultrasound, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Feng GaoDepartment of Medical Ultrasound, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Yang LiuDepartment of Medical Ultrasound, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Xiao-Hui QiaoDepartment of Medical Ultrasound, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Zheng ZhangDepartment of Medical Nephrology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Lian-Fang DuDepartment of Medical Ultrasound, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.ORCID 0000-0002-2825-6708

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Using the model and quantitative parameters of contrast-enhanced ultrasound (CEUS) to assess the severity of secondary hyperparathyroidism (SHPT) was proposed. 42 SHPT patients who underwent CEUS examination were divided into three groups, light, moderate, and heavy as per parathyroid hormone (PTH). The process of CEUS was divided into two phases, wash-in phase and wash-out phase. The three groups were analyzed with their enhancing model in the two phases. The quantitative parameters of CEUS such as Arrival Time (AT), Time to Peak (TTP), Mean Transit Time (MTT), and Maximum Intensity (IMAX) were measured by time-intensity curve (TIC) and compared among the three groups. The enhancing model of light SHPT, moderate SHPT, and heavy SHPT showed statistical significance in wash-in phase and wash-out phase (

Indexed as

AdultAgedAged, 80 and overContrast MediaFemaleHumansHyperparathyroidism, SecondaryImage EnhancementMaleMiddle AgedParathyroid GlandsUltrasonographyContrast Media

Identifiers

PMID28078296
PMCPMC5204084

What OpenQuestion holds

Textmetadata
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.