Evidence map›Paper›PMID 33299412›Full record

ArticleInternational journal of endocrinology2020

Postoperative Nomogram for Predicting Cancer-Specific and Overall Survival among Patients with Medullary Thyroid Cancer.

Li Chen, Yizeng Wang, Ke Zhao, Yuyun Wang, Xianghui He

Open access · goldAbstract read
In one paragraph

Article in International journal of endocrinology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed
2.3field-weighted citation impact, top 10% 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

17 citing papers in PubMed, 21 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

5 authors at 1 institution in 1 country.

Li ChenDepartment of General Surgery, Tianjin Medical University General Hospital, Tianjin Medical University, Tianjin, China.
Yizeng WangDepartment of General Surgery, Tianjin Medical University General Hospital, Tianjin Medical University, Tianjin, China.
Ke ZhaoDepartment of General Surgery, Tianjin Medical University General Hospital, Tianjin Medical University, Tianjin, China.
Yuyun WangDepartment of General Surgery, Tianjin Medical University General Hospital, Tianjin Medical University, Tianjin, China.
Xianghui HeDepartment of General Surgery, Tianjin Medical University General Hospital, Tianjin Medical University, Tianjin, China.ORCID https://orcid.org/0000-0001-9977-162X
Tianjin Medical University General Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundMedullary thyroid carcinoma (MTC) accounts for 1%-2% of thyroid cancer in the United States based on the latest Surveillance, Epidemiology, and End Results (SEER) data, and this study aimed to construct a comprehensive predictive nomogram based on various clinical variables in MTC patients who underwent total thyroidectomy and neck lymph nodes dissection.

methodsData regarding 1,237 MTC patients who underwent total thyroidectomy and neck lymph nodes dissection from 2004 to 2015 were obtained from the SEER database. Univariate and multivariate Cox regression analyses were used to screen for meaningful independent predictors. These independent factors were used to construct a nomogram model, a survival prognostication tool for 3- and 5-year overall survival, and cancer-specific survival among these MTC patients.

resultA total of 1,237 patients enrolled from the SEER database were randomly divided into the training group (

conclusionNomograms constructed by using various clinical variables can make more comprehensive and accurate predictions for MTC patients who underwent total thyroidectomy and neck lymph nodes. These predictive nomograms help identify postoperative high-risk MTC patients and facilitate patient counseling on clinical prognosis and follow-up.

Identifiers

PMID33299412
PMCPMC7704131
OpenAlexW3108802905

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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.