Evidence map›Paper›PMID 36439514›Full record

ReviewFrontiers in oncology2022

Diagnostic value of multiple diagnostic methods for lymph node metastases of papillary thyroid carcinoma: A systematic review and meta-analysis.

Si-Rui Wang, Qiao-Li Li, Feng Tian, Jun Li, Wen-Xiao Li, Ming Chen, Tian Sang, Chun-Li Cao, Li-Nan Shi

Open access · goldAbstract readReview
In one paragraph

Review in Frontiers in oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed, 1 pooled it
1.1field-weighted citation impact, top 20% 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

8 citing papers in PubMed, 1 synthesis or guideline pooled it, 9 citations in OpenAlex.

  1. Guideline
  2. Article
  3. Article
  4. Current evidence and strategies for preventing tumor recurrence following thermal ablation of papillary thyroid carcinoma.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025
    Review
  5. Review
  6. Article
  7. Article
  8. 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

9 authors at 2 institutions in 1 country.

Si-Rui WangDepartment of Ultrasound, the First Affiliated Hospital of Medical College, Shihezi University, Shihezi, Xinjiang, China.
Qiao-Li LiDepartment of Ultrasound, the First Affiliated Hospital of Medical College, Shihezi University, Shihezi, Xinjiang, China.
Feng TianDepartment of Neurology, the First Affiliated Hospital of Medical College, Shihezi University, Shihezi, Xinjiang, China.
Jun LiDepartment of Ultrasound, the First Affiliated Hospital of Medical College, Shihezi University, Shihezi, Xinjiang, China.
Wen-Xiao LiDepartment of Ultrasound, the First Affiliated Hospital of Medical College, Shihezi University, Shihezi, Xinjiang, China.
Ming ChenDepartment of Ultrasound, the First Affiliated Hospital of Medical College, Shihezi University, Shihezi, Xinjiang, China.
Tian SangDepartment of Ultrasound, the First Affiliated Hospital of Medical College, Shihezi University, Shihezi, Xinjiang, China.
Chun-Li CaoDepartment of Ultrasound, the First Affiliated Hospital of Medical College, Shihezi University, Shihezi, Xinjiang, China.
Li-Nan ShiDepartment of Ultrasound, the First Affiliated Hospital of Medical College, Shihezi University, Shihezi, Xinjiang, China.
First Affiliated Hospital of Shihezi University Medical College · CNShihezi University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: This study compared the diagnostic value of various diagnostic methods for lymph node metastasis (LNM) of papillary thyroid carcinoma (PTC) through network meta-analysis. Methods: In this experiment, databases such as CNKI, Wanfang, PubMed, and Web of Science were retrieved according to the Cochrane database, Prisma, and NMAP command manual. A meta-analysis was performed using STATA 15.0, and the value of the surface under the cumulative ranking curve (SUCRA) was used to determine the most effective diagnostic method. Quality assessments were performed using the Cochrane Collaboration's risk of bias tool, and publication bias was assessed using Deeks' funnel plot. Results: A total of 38 articles with a total of 6285 patients were included. A total of 12 diagnostic methods were used to study patients with LNM of PTC. The results showed that 12 studies were direct comparisons and 8 studies were indirect comparisons. According to the comprehensive analysis of the area of SUCRA, US+CT(86.8) had the highest sensitivity, FNAC had the highest specificity (92.4) and true positive predictive value (89.4), and FNAC+FNA-Tg had higher negative predictive value (99.4) and accuracy (86.8). In the non-invasive method, US+CT had the highest sensitivity, and the sensitivity (SEN) was [OR=0.59, 95% confidence interval (CI): (0.30, 0.89]. Among the invasive methods, the combined application of FNAC+FNA-Tg had higher diagnostic performance. The sensitivity was [OR=0.62, 95% CI: (0.26, 0.98)], the specificity (SPE) was [OR=1.12, 95% CI: (0.59, 1.64)], the positive predictive value was [OR=0.98, 95% CI: (0.59, 1.37)], the negative predictive value was [OR=0.64, 95% CI (0.38, 0.90)], and the accuracy was [OR=0.71, 95% CI: (0.31, 1.12)]. Conclusion: In the non-invasive method, the combined application of US+CT had good diagnostic performance, and in the invasive method, the combined application of FNAC+FNA-Tg had high diagnostic performance, and the above two methods were recommended.

Indexed as

diagnostic valuelymph nodes metastasismultiple diagnostic methodsnetwork meta-analysispapillary thyroid carcinoma

Identifiers

PMID36439514
PMCPMC9686443
OpenAlexW4308868314

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.