Evidence map›Paper›PMID 34422249›Full record

ArticleJournal of healthcare engineering2021

Image Risk Assessment of the Thyroid Cancer Model Based on Discriminant Analysis and the Value of TAP and CEA Combined Detection.

Bin Wang, Shengliang Wang, Shaolei Lang, Li Liang, Yang Yang, Yan Xue, Yinxia Xu

RetractedOpen access · hybridAbstract readRetracted Publication
In one paragraph

Article in Journal of healthcare engineering, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact, top 85% 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

0 citing papers in PubMed, 0 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

7 authors at 1 institution in 1 country.

Bin WangSanmenxia Central Hospital of Henan Province, Henan 472000, China.
Shengliang WangSanmenxia Central Hospital of Henan Province, Henan 472000, China.
Shaolei LangSanmenxia Central Hospital of Henan Province, Henan 472000, China.
Li LiangSanmenxia Central Hospital of Henan Province, Henan 472000, China.
Yang YangSanmenxia Central Hospital of Henan Province, Henan 472000, China.
Yan XueSanmenxia Central Hospital of Henan Province, Henan 472000, China.
Yinxia XuSanmenxia Central Hospital of Henan Province, Henan 472000, China.ORCID 0000-0002-7145-4814
Xian Central Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The incidence rate of thyroid disease is increasing rapidly worldwide, and the number of thyroid patients is increasing. In this study, serum TAP (tumor abnormal protein) and CEA (carcinoembryonic antigen) were used to detect patients with thyroid nodules of class IV and above to explore the value of serum TAP combined detection of CEA in the risk assessment of thyroid cancer. In this paper, 400 patients with thyroid nodules above class IV diagnosed by physical examination in our hospital health management center from January 2019 to June 2021 were included in the study. Combined with the pathological test results, the patients were divided into risk groups. At the same time, different groups of serum TAP and CEA levels were detected by aggregation and electrochemiluminescence methods, and serum TAP and CEA levels were analyzed according to the pathological diagnostic indicators of CEA levels. The results showed that the levels of serum TAP and CEA in patients with thyroid cancer were significantly higher than those in patients with benign thyroid diseases, and the difference was statistically significant (

Indexed as

Carcinoembryonic AntigenThyroid NeoplasmsBiomarkers, TumorDiscriminant AnalysisHumansRisk AssessmentBiomarkers, TumorCarcinoembryonic Antigen

Identifiers

PMID34422249
PMCPMC8373514
OpenAlexW3192260583

What OpenQuestion holds

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LicenceCC BY
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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.