Evidence map›Paper›PMID 33969124›Full record

ArticleBioMed research international2021

Application of Carbon Nanoparticles in Neck Dissection of Clinically Node-Negative Papillary Thyroid Carcinoma.

Zhongyan Chen, Zhiming Zhong, Guoqing Chen, Yun Feng

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Article in BioMed research international, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

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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

4 authors.

Zhongyan ChenDepartment of Otolaryngology, China-Japan Friendship Hospital, Beijing 100029, China.ORCID https://orcid.org/0000-0002-4839-0760
Zhiming ZhongDepartment of Otolaryngology, Jianou Municipal Hospital, Fujian 353100, China.
Guoqing ChenDepartment of Otolaryngology, Jinnan Branch of Jinjiang Municipal Hospital, 362241 Fujian, China.ORCID https://orcid.org/0000-0001-6444-0935
Yun FengDepartment of Otolaryngology, China-Japan Friendship Hospital, Beijing 100029, China.ORCID https://orcid.org/0000-0002-9208-8243

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeThe purpose of this retrospective study was to evaluate the advantages of carbon nanoparticles in neck dissection and to conclude its application in the treatment of clinically node-negative papillary thyroid carcinoma (CN0PTC).

methodsAs a retrospective cohort study, we divided the enrolled patients into two groups, the carbon nanoparticle (CN) group and the control group according to the usage of CN. In the CN group, CN was applied to reveal drainage lymph nodes and the picked LNs were sent for fast frozen testing. If metastasis exits, modified radical lateral lymph node dissection (LLND) was performed. For both groups, prophylactic central lymph node dissection was routinely done. Finally, the demographic information, tumor characteristics, postoperative pathological results, and laboratory data were collected for analysis.

resultsA total of 61 CN0PTC were enrolled in this study, 33 in the CN group and 28 in the control group. The black-stained rate for CN was 29/40 (72.5%) with a positive prediction rate of 34.5%. The mainly black-stained region in the lateral neck was level III and possesses the highest lymph node ratio (17.5%). The metastasis that occurred in level VI was 30% and 11.8% in the CN and control groups, respectively (

conclusionThe lateral neck metastasis in patients with papillary thyroid microcarcinoma in clinical stage cT1aN0 is not rare. CN can help surgeons to distinguish the real person who actually needs LLND. In prophylactic CLND, CN acts as a tracer which makes the parathyroid gland more identifiable and avoids risks of injuries to nerves and glands.

Indexed as

Neck DissectionAdultCarbonCase-Control StudiesFemaleHumansLymph NodesMaleNanoparticlesThyroid Cancer, PapillaryThyroid NeoplasmsCarbon

Identifiers

PMID33969124
PMCPMC8084638

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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.