Evidence map›Paper›PMID 37808114›Full record

ArticleFrontiers in veterinary science2023

Can

Suzane Peres Campanholi, Sebastião Garcia Neto, Gabriel Martins Pinheiro, Marcelo Fábio Gouveia Nogueira, José Celso Rocha, João Diego de Agostini Losano, Adriano Felipe Perez Siqueira, Marcílio Nichi, Mayra Elena Ortiz D'Avila Assumpção, Andréa Cristina Basso and 2 more

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in veterinary science, 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
1.9field-weighted citation impact, top 14% 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

4 citing papers in PubMed, 7 citations in OpenAlex.

  1. Article
  2. Review
  3. Review
  4. Review
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

12 authors at 3 institutions in 1 country.

Suzane Peres CampanholiDepartamento de Patologia, Reprodução e Saúde Única, Faculdade de Ciências Agrárias e Veterinárias (FCAV), Universidade Estadual Paulista, Jaboticabal, Brazil.
Sebastião Garcia NetoSenepol 3G, Barretos, Brazil.
Gabriel Martins PinheiroDepartamento de Ciências Biológicas, Faculdade de Ciências e Letras (FCLA), Universidade Estadual Paulista (UNESP), Assis, Brazil.
Marcelo Fábio Gouveia NogueiraDepartamento de Ciências Biológicas, Faculdade de Ciências e Letras (FCLA), Universidade Estadual Paulista (UNESP), Assis, Brazil.
José Celso RochaDepartamento de Ciências Biológicas, Faculdade de Ciências e Letras (FCLA), Universidade Estadual Paulista (UNESP), Assis, Brazil.
João Diego de Agostini LosanoDepartamento de Reprodução Animal, Faculdade de Medicina Veterinária e Zootecnia (FMVZ), Universidade de São Paulo (USP), São Paulo, Brazil.
Adriano Felipe Perez SiqueiraDepartamento de Reprodução Animal, Faculdade de Medicina Veterinária e Zootecnia (FMVZ), Universidade de São Paulo (USP), São Paulo, Brazil.
Marcílio NichiDepartamento de Reprodução Animal, Faculdade de Medicina Veterinária e Zootecnia (FMVZ), Universidade de São Paulo (USP), São Paulo, Brazil.
Mayra Elena Ortiz D'Avila AssumpçãoDepartamento de Reprodução Animal, Faculdade de Medicina Veterinária e Zootecnia (FMVZ), Universidade de São Paulo (USP), São Paulo, Brazil.
Andréa Cristina BassoIn Vitro Brasil SA, Mogi Mirim, Brazil.
Fabio Morato MonteiroCentro Avançado de Pesquisa de Bovinos de Corte, Agência Paulista de Tecnologia dos Agronegócios/Instituto de Zootecnia (APTA/IZ), Sertãozinho, Brazil.
Lindsay Unno GimenesDepartamento de Patologia, Reprodução e Saúde Única, Faculdade de Ciências Agrárias e Veterinárias (FCAV), Universidade Estadual Paulista, Jaboticabal, Brazil.
Universidade Estadual Paulista (Unesp) · BRUniversidade de São Paulo · BRInstitute of Animal Science and Pastures · BR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Thoroughly analyzing the sperm and exploring the information obtained using artificial intelligence (AI) could be the key to improving fertility estimation. Artificial neural networks have already been applied to calculate zootechnical indices in animals and predict fertility in humans. This method of estimating the results of reproductive biotechnologies, such as

Indexed as

artificial intelligencebovinefertilityIVEPsperm kinetics

Identifiers

PMID37808114
PMCPMC10551135
OpenAlexW4386988746

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.