Evidence map›Paper›PMID 31919768›Full record

ArticleEndocrine2020

Learnability and reproducibility of ACR Thyroid Imaging, Reporting and Data System (TI-RADS) in postgraduate freshmen.

Dengke Teng, Ping Fu, Wenjia Li, Feng Guo, Hui Wang

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Article in Endocrine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

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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

5 citing papers in PubMed.

  1. Observational
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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.

Dengke TengDepartment of Ultrasound, China-Japan Union Hospital of Jilin University, Changchun, 130000, Jilin, China.
Ping FuDepartment of Ultrasound, The First Affiliated Hospital of Nanchang University, Nanchang, 330006, Jiangxi, China.
Wenjia LiDepartment of Breast Surgery, China-Japan Union Hospital of Jilin University, Changchun, 130000, Jilin, China.
Feng GuoDepartment of Ultrasound, China-Japan Union Hospital of Jilin University, Changchun, 130000, Jilin, China. guofeng_tdk@163.com.
Hui WangDepartment of Ultrasound, China-Japan Union Hospital of Jilin University, Changchun, 130000, Jilin, China. wanghui_tdk@163.com.ORCID http://orcid.org/0000-0001-6729-8292

Funding

The Chinese Ultrasound Doctors Association KJXX2019002
6 · The paper itself

Abstract

purposeThe Thyroid Imaging, Reporting and Data System (TI-RADS) from the American College of Radiology (ACR) has been used since 2017 for the evaluation of thyroid nodules. The purpose of this study is to assess the learnability and reproducibility of TI-RADS in postgraduate freshmen.

methodsThis was a retrospective study involving 400 nodules with a final diagnosis following ultrasound (US) examination. The nodules were randomized into eight groups (50/group). Three postgraduate freshmen and three experts evaluated the nodules according to ACR TI-RADS without knowledge of the final diagnosis. After evaluating each group, training was carried out based on the inconsistencies of the freshmen/experts. Training was stopped after 200 nodules because the κ value showed almost perfect concordance. Three months later, the 50 nodules of Group 4 (the last evaluated group) were re-evaluated to assess the reproducibility of ACR TI-RADS.

resultsThe diagnostic accuracy of the three postgraduate freshmen increased from 60%, 48%, and 46% in Group 1 to 80%, 78%, and 72% in Group 4 (P= 0.029, 0.002, and 0.008), respectively. After training, the diagnostic accuracy of the postgraduate freshmen was close to that of the experts (84%). For the US features, the postgraduate freshmen were consistent with the experts (all κ > 0.6). When re-evaluating Group 4 three months later, the five features had substantial to almost perfect agreement for the same researcher (all κ > 0.7).

conclusionBased on experts' consensus, ACR TI-RADS can be learned well, and its reproducibility is excellent.

Indexed as

Thyroid NoduleData SystemsHumansReproducibility of ResultsRetrospective StudiesUltrasonographyDiagnosisEducationLearning curveThyroid noduleTI-RADSUltrasound

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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.