Evidence map›Paper›PMID 38027237›Full record

ArticleCancer management and research2023

Combining Clinicopathologic and Ultrasonic Features for Predicting Skip Metastasis of Lateral Lymph Nodes in Papillary Thyroid Carcinoma.

Xin Zhang, Ying Chen, Wanyin Chen, Zheng Zhang

Open access · goldAbstract read
In one paragraph

Article in Cancer management and research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
0.6field-weighted citation impact, top 27% 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

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3 · Its place in the literature

Who cites it

4 citing papers in PubMed, 3 citations in OpenAlex.

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4 · The record

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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 at 1 institution in 1 country.

Xin Zhang *Department of Medical Ultrasound, Affiliated Hospital of Jiangsu University, Zhenjiang, 212000, People's Republic of China.
Ying Chen *Department of Medical Pharmacy, Affiliated Hospital of Jiangsu University, Zhenjiang, 212000, People's Republic of China.
Wanyin ChenDepartment of Medical Gynecology, Affiliated Hospital of Jiangsu University, Zhenjiang, 212000, People's Republic of China.
Zheng ZhangDepartment of Medical Ultrasound, Affiliated Hospital of Jiangsu University, Zhenjiang, 212000, People's Republic of China.
Jiangsu University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Skip metastasis, regarded as lateral lymph node metastasis (LLNM) without involving the central lymph node metastasis (CLNM), in papillary thyroid carcinoma (PTC) patients is commonly unpredictable. The purpose of the present research was to investigate the independent risk factors of skip metastasis in patients with PTC. Methods and Materials: In the present research, 228 consecutive PTC patients who experienced total thyroidectomy coupled with central and lateral lymph node dissection from May 2020 to September 2022 at the Affiliated hospital of Jiangsu University were included in our research. Univariate and multivariate analysis were then applied to investigate the risk factors of skip metastasis in patients with PTC. Furthermore, a predictive model of skip metastasis was then constructed based on risk factors. Results: The skip metastasis rate was 11.8% (27/228) in the current research. After the univariate and multivariate analysis, tumor size ≤ 10 mm, unilaterality, microcalcification, and upper tumor location were determined to be predictive factors of skip metastasis. The risk score of skip metastasis was calculated: risk score = 1.229 × (if tumor nodule ≤ 10mm) + 1.518 × (if unilaterality nodule) + 1.074 × (if microcalcification in nodule) + 2.332 × (if nodule in upper location). Conclusion: Tumor size ≤ 10 mm, unilaterality, microcalcification, and upper tumor location can increase the occurrence of skip metastasis in patients with PTC, which is expected to provide useful information to guide the suitable intraoperative window.

Indexed as

clinicopathologic factorslateral lymph node metastasispapillary thyroid carcinomaskip metastasisultrasonic features

Identifiers

PMID38027237
PMCPMC10657546
OpenAlexW4388705911

What OpenQuestion holds

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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.