Evidence map›Paper›PMID 29963163›Full record

ArticleOncology letters2018

Comparative study on the diagnostic values of different ultrasound technologies for malignant thyroid nodules.

Xiao-Zhu Jin, Wei-Wei Lu, Hai-Feng Zhang, Yuan-Yuan Yan, Xiao-Le Gu

Abstract read
In one paragraph

Article in Oncology letters, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 1 pooled it
–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

4 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. 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.

Xiao-Zhu JinDepartment of Ultrasonography, Zhengzhou Central Hospital Affiliated to Zhengzhou University, Zhengzhou, Henan 450000, P.R. China.
Wei-Wei LuDepartment of Ultrasonography, The First People's Hospital of Shangqiu, Shangqiu, Henan 476000, P.R. China.
Hai-Feng ZhangDepartment of Radiology, Zhengzhou Central Hospital Affiliated to Zhengzhou University, Zhengzhou, Henan 450000, P.R. China.
Yuan-Yuan YanDepartment of Ultrasonography, Zhengzhou Central Hospital Affiliated to Zhengzhou University, Zhengzhou, Henan 450000, P.R. China.
Xiao-Le GuDepartment of Ultrasonography, Zhengzhou Central Hospital Affiliated to Zhengzhou University, Zhengzhou, Henan 450000, P.R. China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The present study compared and analyzed the diagnostic values of B-mode ultrasound and elasticity imaging technology for the identification of benign and malignant thyroid nodules. Ninety-four patients who were diagnosed with thyroid nodules by ultrasound were chosen. All patients were checked with B-mode ultrasound and ultrasound elasticity imaging technology before surgeries. Further, the post-operative outcomes were compared with operation pathology. The pathological examination results were taken as the gold standard. The accuracy, sensitivity, specificity, positive predictive value and negative predictive values of combined ultrasound diagnosis were all obviously higher than those by the B-mode ultrasound and the ultrasound elastography method (P<0.05). In conclusion, as a new technology, ultrasound elasticity imaging technology has relatively high diagnositic value in identifying benign and malignant thyroid nodules. With its own advantages and limitations, the ultrasound elasticity imaging technology could effectively complement B-mode ultrasound, and improve the sensitivity, specificity and accuracy of ultrasound diagnosis for thyroid nodules.

Indexed as

benignB-mode ultrasoundelasticity imaging techniquesmalignantthyroid nodule

Identifiers

PMID29963163
PMCPMC6019883

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Registered trials

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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.