Evidence map›Paper›PMID 40807083›Full record

ArticleJournal of clinical medicine2025

A Novel Deep Learning Model for Predicting Colorectal Anastomotic Leakage: A Pioneer Multicenter Transatlantic Study.

Miguel Mascarenhas, Francisco Mendes, Filipa Fonseca, Eduardo Carvalho, Andre Santos, Daniela Cavadas, Guilherme Barbosa, Antonio Pinto da Costa, Miguel Martins, Abdullah Bunaiyan and 8 more

Abstract read
In one paragraph

Article in Journal of clinical medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Review
  2. Article
  3. Review
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  5. Review
  6. 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

18 authors.

Miguel MascarenhasPrecision Medicine Unit, Department of Gastroenterology, Unidade Local de Saúde São João, 4200-319 Porto, Portugal.ORCID 0000-0002-0340-0830
Francisco MendesPrecision Medicine Unit, Department of Gastroenterology, Unidade Local de Saúde São João, 4200-319 Porto, Portugal.ORCID 0000-0002-5890-7049
Filipa FonsecaInstituto Português de Oncologia de Lisboa, 1099-023 Lisboa, Portugal.ORCID 0000-0002-9003-3871
Eduardo CarvalhoINEGI-Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial, 4200-465 Porto, Portugal.ORCID 0009-0005-0729-7008
Andre SantosFaculty of Medicine, University of Porto, 4200-427 Porto, Portugal.ORCID 0000-0002-9676-6128
Daniela CavadasInstituto Português de Oncologia de Lisboa, 1099-023 Lisboa, Portugal.
Guilherme BarbosaINEGI-Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial, 4200-465 Porto, Portugal.ORCID 0009-0005-5001-5539
Antonio Pinto da CostaDigestAID, Digestive Artificial Intelligence Development, 4200-135 Porto, Portugal.
Miguel MartinsPrecision Medicine Unit, Department of Gastroenterology, Unidade Local de Saúde São João, 4200-319 Porto, Portugal.
Abdullah BunaiyanRoyal Liverpool University Hospital, Liverpool L7 8YE, UK.ORCID 0009-0002-7857-2415
Maísa VasconcelosHospital das Clinicas da Faculdade de Medicina da Universidade de São Paulo, Clínica de Ribeirao Preto, Ribeirao 05403-010, Brazil.
Marley Ribeiro FeitosaHospital das Clinicas da Faculdade de Medicina da Universidade de São Paulo, Clínica de Ribeirao Preto, Ribeirao 05403-010, Brazil.ORCID 0000-0002-4440-2023
Shay WilloughbyRoyal Liverpool University Hospital, Liverpool L7 8YE, UK.ORCID 0000-0003-2743-7918
Shakil AhmedRoyal Liverpool University Hospital, Liverpool L7 8YE, UK.
Muhammad Ahsan JavedRoyal Liverpool University Hospital, Liverpool L7 8YE, UK.ORCID 0000-0003-0402-2285
Nilza RamiãoINEGI-Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial, 4200-465 Porto, Portugal.
Guilherme MacedoPrecision Medicine Unit, Department of Gastroenterology, Unidade Local de Saúde São João, 4200-319 Porto, Portugal.ORCID 0000-0002-9387-9872
Manuel LimbertInstituto Português de Oncologia de Lisboa, 1099-023 Lisboa, Portugal.ORCID 0000-0002-4645-1574

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

PubMed holds no abstract for this paper.

Indexed as

anastomosis leakageartificial intelligencedeep learning

Identifiers

PMID40807083
PMCPMC12347578

What OpenQuestion holds

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LicenceCC BY
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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.