Evidence map›Paper›PMID 35202324›Full record

ArticleVeterinary sciences2022

Vet-OncoNet: Developing a Network of Veterinary Oncology and Reporting a Pioneering Portuguese Experience.

Katia Pinello, Isabel Pires, Ana Filipa Castro, Paulo Tiago Carvalho, Andreia Santos, Augusto de Matos, Felisbina Queiroga, João Niza-Ribeiro

Open access · goldAbstract read
In one paragraph

Article in Veterinary sciences, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
2.1field-weighted citation impact, top 12% 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

11 citing papers in PubMed, 14 citations in OpenAlex.

  1. Review
  2. Article
  3. Article
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  5. Review
  6. Article
  7. Article
  8. Canine Mammary Cancer: State of the Art and Future Perspectives.Animals : an open access journal from MDPI · 2023
    Review
  9. Article
  10. Article
  11. Article
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

8 authors at 2 institutions in 1 country.

Katia PinelloVet-OncoNet, Departamento de Estudo de Populações, ICBAS, Instituto de Ciências Biomédicas Abel Salazar, Universidade do Porto, Rua de Jorge Viterbo Ferreira 228, 4050-313 Porto, Portugal.ORCID 0000-0002-0870-6281
Isabel PiresDepartamento de Ciências Veterinárias, Universidade Trás-os-Montes e Alto Douro (UTAD), Quinta de Prados, 5000-801 Vila Real, Portugal.ORCID 0000-0001-6330-4560
Ana Filipa CastroVet-OncoNet, Departamento de Estudo de Populações, ICBAS, Instituto de Ciências Biomédicas Abel Salazar, Universidade do Porto, Rua de Jorge Viterbo Ferreira 228, 4050-313 Porto, Portugal.
Paulo Tiago CarvalhoVet-OncoNet, Departamento de Estudo de Populações, ICBAS, Instituto de Ciências Biomédicas Abel Salazar, Universidade do Porto, Rua de Jorge Viterbo Ferreira 228, 4050-313 Porto, Portugal.
Andreia SantosDepartamento de Clínicas Veterinárias, ICBAS, Instituto de Ciências Biomédicas de Abel Salazar, Universidade do Porto, Rua de Jorge Viterbo Ferreira 228, 4050-313 Porto, Portugal.
Augusto de MatosDepartamento de Clínicas Veterinárias, ICBAS, Instituto de Ciências Biomédicas de Abel Salazar, Universidade do Porto, Rua de Jorge Viterbo Ferreira 228, 4050-313 Porto, Portugal.
Felisbina QueirogaDepartamento de Ciências Veterinárias, Universidade Trás-os-Montes e Alto Douro (UTAD), Quinta de Prados, 5000-801 Vila Real, Portugal.ORCID 0000-0001-6130-8381
João Niza-RibeiroVet-OncoNet, Departamento de Estudo de Populações, ICBAS, Instituto de Ciências Biomédicas Abel Salazar, Universidade do Porto, Rua de Jorge Viterbo Ferreira 228, 4050-313 Porto, Portugal.ORCID 0000-0002-6413-1056
Universidade do Porto · PTUniversity of Trás-os-Montes and Alto Douro · PT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Vet-OncoNet is a replicable tripartite animal cancer database with the scientific and academic purposes of collecting data and producing evidence-based knowledge for cancer science in general. Inspired by the One Health vision, Vet-OncoNet uses business intelligence tools to optimize the process of capturing, treating, and reporting animal cancer data to a national level in three interfaces: ACR (animal cancer registry, pathology-based), COR (clinical oncology registry, vet practice-based) and RFR (risk factor registry, owner-based). The first results show that skin and mammary gland are by far the most affected systems. Mast cell tumors and complex adenoma of the mammary gland are the most frequent histologic type in dogs, while in cats they are squamous cell carcinomas, tubular adenocarcinoma of the mammary gland and lymphomas. Regarding COR, it provides valuable information on the landscape of veterinary oncology practices, therapeutics options, outcomes and owners' drivers' adherence towards therapies, which range from 30% up to 80% upon vet practices. Furthermore, being aware of the role of animals within the family and as possible sentinels of environmental risks to cancer in humans, the network built an interface (Pet-OncoNet) dedicated to owners and a database (RFR) that receives information regarding pets and owners' daily habits.

Indexed as

cancercomparative oncologydatabaseepidemiologyveterinary

Identifiers

PMID35202324
PMCPMC8877992
OpenAlexW4210853803

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.