Evidence map›Paper›PMID 33748206›Full record

ArticleFrontiers in veterinary science2021

Diagnostic Accuracy of Delayed Phase Post Contrast Computed Tomographic Images in the Diagnosis of Focal Liver Lesions in Dogs: 69 Cases.

Silvia Burti, Alessandro Zotti, Federico Bonsembiante, Barbara Contiero, Tommaso Banzato

Erratum issuedOpen access · goldAbstract read
In one paragraph

Article in Frontiers in veterinary science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
1.2field-weighted citation impact, top 21% 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

7 citing papers in PubMed, 14 citations in OpenAlex.

  1. Article
  2. Article
  3. A Veterinary DICOM-Based Deep Learning Denoising Algorithm Can Improve Subjective and Objective Brain MRI Image Quality.Veterinary radiology & ultrasound : the official journal of the American College of Veterinary Radiology and the International Veterinary Radiology Association · 2025
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  5. Review
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

5 authors at 1 institution in 1 country.

Silvia BurtiDepartment of Animal Medicine, Productions and Health, University of Padua, Padua, Italy.
Alessandro ZottiDepartment of Animal Medicine, Productions and Health, University of Padua, Padua, Italy.
Federico BonsembianteDepartment of Animal Medicine, Productions and Health, University of Padua, Padua, Italy.
Barbara ContieroDepartment of Animal Medicine, Productions and Health, University of Padua, Padua, Italy.
Tommaso BanzatoDepartment of Animal Medicine, Productions and Health, University of Padua, Padua, Italy.
University of Padua · IT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

To describe the computed tomographic (CT) features of focal liver lesions (FLLs) in dogs, that could enable predicting lesion histotype. Dogs diagnosed with FLLs through both CT and cytopathology and/or histopathology were retrospectively collected. Ten qualitative and 6 quantitative CT features have been described for each case. Lastly, a machine learning-based decision tree was developed to predict the lesion histotype. Four categories of FLLs - hepatocellular carcinoma (HCC,

Indexed as

computed tomogaphycontrast - enhanced CTdecision treefocal liver lesionHCC (hepatic cellular carcinoma)

Identifiers

PMID33748206
PMCPMC7969650
OpenAlexW3134207903

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.