Evidence map›Paper›PMID 39422217›Full record

ArticleVeterinary pathology2025

Artificial intelligence can be trained to predict

Chloé Puget, Jonathan Ganz, Julian Ostermaier, Thomas Conrad, Eda Parlak, Christof A Bertram, Matti Kiupel, Katharina Breininger, Marc Aubreville, Robert Klopfleisch

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Veterinary pathology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Artificial intelligence predictsVeterinary pathology · 2026
    Article
  2. 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

10 authors.

Chloé PugetFreie Universität Berlin, Berlin, Germany.
Jonathan GanzTechnische Hochschule Ingolstadt, Ingolstadt, Germany.ORCID 0009-0008-1299-8716
Julian OstermaierTechnische Hochschule Ingolstadt, Ingolstadt, Germany.
Thomas ConradFreie Universität Berlin, Berlin, Germany.
Eda ParlakUniversity of Veterinary Medicine, Vienna, Austria.
Christof A BertramUniversity of Veterinary Medicine, Vienna, Austria.ORCID 0000-0002-2402-9997
Matti KiupelMichigan State University, East Lansing, MI.
Katharina BreiningerFriedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.
Marc AubrevilleTechnische Hochschule Ingolstadt, Ingolstadt, Germany.ORCID 0000-0002-5294-5247
Robert KlopfleischFreie Universität Berlin, Berlin, Germany.ORCID 0000-0002-6308-0568

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Numerous prognostic factors are currently assessed histologically and immunohistochemically in canine mast cell tumors (MCTs) to evaluate clinical behavior. In addition, polymerase chain reaction (PCR) is often performed to detect internal tandem duplication (ITD) mutations in exon 11 of the

Indexed as

Artificial IntelligenceDog DiseasesProto-Oncogene Proteins c-kitAnimalsDeep LearningDogsEosine Yellowish-(YS)HematoxylinMutationSensitivity and SpecificityStaining and LabelingEosine Yellowish-(YS)HematoxylinProto-Oncogene Proteins c-kitartificial intelligencecanine cutaneous mast cell tumorc-KIT mutationconvolutional neural networkdeep learningdigital pathologyexon 11genotype predictionmachine learning

Identifiers

PMID39422217
PMCPMC11874474

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.