Evidence map›Paper›PMID 35812867›Full record

ArticleFrontiers in veterinary science2022

Epidemiological Investigation of Canine Mammary Tumors in Mainland China Between 2017 and 2021.

Hui-Hua Zheng, Chong-Tao Du, Chao Yu, Yu-Zhu Zhang, Rong-Lei Huang, Xin-Yue Tang, Guang-Hong Xie

Abstract read
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Article in Frontiers in veterinary science, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.

0numbers the graph read from it
0cells of the map it votes in
27citing papers in PubMed
–field-weighted citation impact
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

27 citing papers in PubMed.

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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

7 authors.

Hui-Hua ZhengDepartment of Clinical Veterinary Medicine, College of Veterinary Medicine, Jilin University, Changchun, China.
Chong-Tao DuDepartment of Clinical Veterinary Medicine, College of Veterinary Medicine, Jilin University, Changchun, China.
Chao YuDepartment of Clinical Veterinary Medicine, College of Veterinary Medicine, Jilin University, Changchun, China.
Yu-Zhu ZhangDepartment of Clinical Veterinary Medicine, College of Veterinary Medicine, Jilin University, Changchun, China.
Rong-Lei HuangDepartment of Clinical Veterinary Medicine, College of Veterinary Medicine, Jilin University, Changchun, China.
Xin-Yue TangDepartment of Clinical Veterinary Medicine, College of Veterinary Medicine, Jilin University, Changchun, China.
Guang-Hong XieDepartment of Clinical Veterinary Medicine, College of Veterinary Medicine, Jilin University, Changchun, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Epidemiological studies enable us to analyze disease behavior, define risk factors, and establish fundamental prognostic criteria. This study aimed to determine the epidemiological and clinical characteristics of canine tumors diagnosed during the years 2017-2021. The results showed that canine mammary tumors were the most common tumors, and their relative incidence for 5-years-total was 46.71% (504/1,079), with 48.41% (244/504) of benign, and 51.59% (260/504) of malignant. Pure breeds accounted for 84.13% (424/504) of submissions, and adult female dogs (9-12 years old) were most frequently involved, followed by 5-8-year-old females. Remarkably, 2.58% (13/504) occurred in the male dogs. In addition, a high prevalence of mammary tumors (77.38%, 390/504) was diagnosed in unneutered dogs, and different incidence rates were observed in different regions (Northeast, Southeast, Northwest and Southwest China). For clinical factors, the tumor size ranged from 0.5 to 28 cm, with the 0-5 cm being the most common tumor size (47.82%, 241/504), and malignant tumors (4.33 ± 2.88 cm, mean ± SD) were bigger than benign ones (3.06 ± 1.67 cm, mean ± SD) (

Indexed as

benigncanine mammary tumorsclinicalepidemiologymalignant

Identifiers

PMID35812867
PMCPMC9257276

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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.