Evidence map›Paper›PMID 37560722›Full record

ReviewArchives of medical science : AMS2023

Diagnostic potential of ultrasonography and computed tomography in differentiating cervical lymph node metastasis of thyroid cancer: a systematic review and meta-analysis.

Yong-Hui Chen, Ying-Qiang Zhang

Abstract readReview
In one paragraph

Review in Archives of medical science : AMS, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  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

2 authors.

Yong-Hui ChenDepartment of Nuclear Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, China.
Ying-Qiang ZhangDepartment of Nuclear Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Ultrasonography (US) and computed tomography (CT) are the most common diagnostic modalities of cervical lymph node metastasis of thyroid cancer, but few studies have been conducted to compare their diagnostic accuracy, with inconclusive results. Material and methods: Multiple databases including PubMed, Springer, EMBASE, Ovid, and the Cochrane Library were searched with the keywords "thyroid cancer OR thyroid carcinomas", "cervical lymph nodes", "metastatic OR metastasis", and "ultrasonography OR ultrasound OR CT OR computed tomography" in June 2018. Full-text articles comparing diagnostic accuracy of US and CT were reviewed. Meta-analyses were conducted to estimate sensitivity and specificity. The forest plots of sensitivity and specificity and summary receiver operating characteristic curves (SROC) are also presented in this article. Results: Finally, 8 of 1785 studies which eventually met the inclusion criteria were selected in this study. The mean sensitivities and specificities of CT in whole and central cervical areas were 0.65, 0.56 and 0.89, 0.83, respectively, while for US, the sensitivities and specificities were 0.58, 0.39 and 0.89, 0.91, respectively. The area under the curve (AUCs) observed of CT and US in whole, central and lateral cervical areas were 0.79 vs. 0.79, and 0.76 vs. 0.67. Because only a few articles were included in this study, publication bias was not assessed. Conclusions: The diagnostic accuracy of US and CT was comparable. The specificity of these two methods was much higher than the sensitivity.

Indexed as

cervicaldiagnosismetastasisthyroid cancer

Identifiers

PMID37560722
PMCPMC10408019

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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.