Evidence map›Paper›PMID 34306071›Full record

ArticleInternational journal of endocrinology2021

A Prediction Model for Contralateral Central Neck Lymph Node Metastases in Unilateral Papillary Thyroid Cancer.

Hai-Long Tan, Bo-Qiang Huang, Gui-You Li, Bo Wei, Pei Chen, Hui-Yu Hu, Mian Liu, Deng-Jie Ou-Yang, Qiong Yang, Zi-En Qin and 4 more

Open access · goldAbstract read
In one paragraph

Article in International journal of endocrinology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
1.2field-weighted citation impact, top 21% 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

6 citing papers in PubMed, 10 citations in OpenAlex.

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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

14 authors at 1 institution in 1 country.

Hai-Long TanDepartment of General Surgery, Xiangya Hospital Central South University, Changsha 410008, Hunan, China.ORCID https://orcid.org/0000-0002-3345-5277
Bo-Qiang HuangDepartment of General Surgery, Xiangya Hospital Central South University, Changsha 410008, Hunan, China.
Gui-You LiDepartment of General Surgery, Xiangya Hospital Central South University, Changsha 410008, Hunan, China.ORCID https://orcid.org/0000-0003-2207-6033
Bo WeiDepartment of General Surgery, Xiangya Hospital Central South University, Changsha 410008, Hunan, China.ORCID https://orcid.org/0000-0001-8882-0107
Pei ChenDepartment of General Surgery, Xiangya Hospital Central South University, Changsha 410008, Hunan, China.ORCID https://orcid.org/0000-0002-6495-3025
Hui-Yu HuDepartment of General Surgery, Xiangya Hospital Central South University, Changsha 410008, Hunan, China.ORCID https://orcid.org/0000-0003-2866-9358
Mian LiuDepartment of General Surgery, Xiangya Hospital Central South University, Changsha 410008, Hunan, China.ORCID https://orcid.org/0000-0002-4029-6948
Deng-Jie Ou-YangDepartment of General Surgery, Xiangya Hospital Central South University, Changsha 410008, Hunan, China.ORCID https://orcid.org/0000-0002-7235-9835
Qiong YangDepartment of General Surgery, Xiangya Hospital Central South University, Changsha 410008, Hunan, China.ORCID https://orcid.org/0000-0002-4743-934X
Zi-En QinDepartment of General Surgery, Xiangya Hospital Central South University, Changsha 410008, Hunan, China.ORCID https://orcid.org/0000-0001-9697-2119
Qi-Man ShiDepartment of General Surgery, Xiangya Hospital Central South University, Changsha 410008, Hunan, China.ORCID https://orcid.org/0000-0002-2355-5012
Ning LiDepartment of General Surgery, Xiangya Hospital Central South University, Changsha 410008, Hunan, China.ORCID https://orcid.org/0000-0002-3477-2194
Peng HuangDepartment of General Surgery, Xiangya Hospital Central South University, Changsha 410008, Hunan, China.ORCID https://orcid.org/0000-0003-3011-3359
Shi ChangDepartment of General Surgery, Xiangya Hospital Central South University, Changsha 410008, Hunan, China.ORCID https://orcid.org/0000-0001-5291-0151
Central South University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The health problems caused by the frequent relapse of papillary thyroid carcinoma (PTC) remain a worldwide concern since the morbidity rate of PTC ranks the highest among thyroid cancers. Residues from contralateral central lymph node metastases (con-CLNM) are the key reason for persistence or recurrence of unilateral papillary thyroid carcinoma (uni-PTC); however, the ability to assess the status of con-CLNM in uni-PTC patients is limited. To clarify the risk factors of con-CLNM, a total of 250 patients with uni-PTC who underwent total thyroidectomy and bilateral central lymph node dissection were recruited in this study. We compared the clinical, sonographic, and pathological characteristics of patients with con-CLNM to those without con-CLNM and established a nomogram for con-CLNM in uni-PTC. We found that male sex, without Hashimoto's thyroiditis, present capsular invasion, with ipsilateral lateral lymph node metastases, and the ratio of ipsilateral central lymph node metastases ≥0.16 were independent con-CLNM predictors of uni-PTC (ORs: 2.797, 0.430, 2.538, 2.202, and 26.588; 95% CIs: 1.182-6.617, 0.211-0.876, 1.223-5.267, 1.064-4.557, and 7.596-93.069, respectively). Additionally, a preoperative nomogram for the prediction of con-CLNM based on these risk factors showed good discrimination (C-index 0.881; 95% CI: 0.840-0.923; sensitivity 85.3%; specificity 76.0%) and good agreement via the calibration plot. Our study provided a way to quantitatively and accurately predict whether con-CLNM occurred in patients with uni-PTC, which may guide surgeons to evaluate the nodal status and perform tailored therapeutic central lymph node dissection.

Identifiers

PMID34306071
PMCPMC8263281
OpenAlexW3174221920

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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.