Evidence map›Paper›PMID 31806992›Full record

ArticleOncoTargets and therapy2019

Individualized Prediction Of Metastatic Involvement Of Lymph Nodes Posterior To The Right Recurrent Laryngeal Nerve In Papillary Thyroid Carcinoma.

Jiang Zhu, Rui Huang, DaiXing Hu, Yi Dou, HaoYu Ren, ZhiXin Yang, Chang Deng, Wei Xiong, Denghui Wang, Yu Mao and 2 more

Open access · goldAbstract read
In one paragraph

Article in OncoTargets and therapy, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
1.3field-weighted citation impact, top 18% 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

13 citing papers in PubMed, 14 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

12 authors at 1 institution in 1 country.

Jiang ZhuDepartment of Endocrine and Breast Surgery, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, People's Republic of China.
Rui HuangDepartment of Anesthesiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, People's Republic of China.
DaiXing HuDepartment of Endocrine and Breast Surgery, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, People's Republic of China.
Yi DouDepartment of Endocrine and Breast Surgery, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, People's Republic of China.
HaoYu RenDepartment of Endocrine and Breast Surgery, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, People's Republic of China.
ZhiXin YangDepartment of Endocrine and Breast Surgery, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, People's Republic of China.
Chang DengDepartment of Endocrine and Breast Surgery, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, People's Republic of China.
Wei XiongDepartment of Endocrine and Breast Surgery, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, People's Republic of China.
Denghui WangDepartment of Endocrine and Breast Surgery, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, People's Republic of China.
Yu MaoDepartment of Endocrine and Breast Surgery, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, People's Republic of China.
Xuesong LiDepartment of Endocrine and Breast Surgery, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, People's Republic of China.
XinLiang SuDepartment of Endocrine and Breast Surgery, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, People's Republic of China.ORCID 0000-0001-5792-1407
The Affiliated Yongchuan Hospital of Chongqing Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeWe aimed to establish a prediction model based on preoperative clinicopathologic features and intraoperative frozen section examination for precise prediction of metastatic involvement of lymph nodes posterior to the right recurrent laryngeal nerve (LN-prRLN) in patients with papillary thyroid carcinoma (PTC).

methodsClinicopathologic data pertaining to patients with PTC who underwent initial thyroid surgery between July 2015 and December 2017 were collected from electronic medical records. Multivariate logistic regression analysis was performed to identify independent predictors of LN-prRLN metastasis for incorporation in the nomogram. The performance of the model was assessed using discriminative ability, calibration, and clinical application.

resultsA total of 592 patients were enrolled in this study. The LN-prRLN metastatic positivity was 19% (95% confidence interval [CI], 15.61-21.89%). On multivariate analysis, ultrasonography-reported LN status, extrathyroid extension, Delphian lymph node metastasis, and number of metastatic pretracheal and paratracheal lymph nodes were independent predictors of LN-prRLN metastasis. The nomogram showed good discriminative ability (C-index: 0.87; [95% CI, 0.84-0.91]; bias-corrected C-index: 0.86 [through bootstrapping validation]) and was well calibrated. The decision curve analysis indicated potential clinical usefulness of the nomogram.

conclusionThis study demonstrates that the risk of LN-prRLN metastasis in individual patients can be robustly predicted by a nomogram that integrates readily available preoperative clinicopathologic features and intraoperative frozen section examination. The nomogram may facilitate intraoperative decision-making for patients with PTC.

Indexed as

lymph node metastasislymph nodes posterior to the right recurrent laryngeal nervenomogrampapillary thyroid carcinomapredict model

Identifiers

PMID31806992
PMCPMC6842273
OpenAlexW2984426793

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.