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Record W2620750994

Reverse computer tomographic (CT) pattern of vacuolar hepatopathy with fat accumulation in canine and feline liver

2016· article· en· W2620750994 on OpenAlexaboutno aff
Angelo Carloni, Michaela Paninárová, Elena Venturelli, Damiano Cavina, Giulia Albarello, F. Arboit, Simone Teodori, Mariarita Romanucci, Leonardo Della Salda, Massimo Vignoli

Bibliographic record

VenueUniTERAMO Research Catalog (University of Teramo) · 2016
Typearticle
Languageen
FieldVeterinary
TopicVeterinary Medicine and Surgery
Canadian institutionsnot available
Fundersnot available
KeywordsComputed tomographicAdipose tissuePathologyMedicineComputed tomographyRadiologyInternal medicine
DOInot available

Abstract

fetched live from OpenAlex

Introduction/purpose
\nDifferent conditions can lead to severe vacuolar hepatopathy with fat accumulation in
\ncanine and feline liver. Hepatic lipidosis, in cats, usually results from chronic anorexia
\nand weight loss due to different causes. In dogs, steatosis is generally associated with
\ndiabetes mellitus and with hepatocellular vacuolar changes occurring in canine
\nhyperadrenocorticism. The definitive diagnosis is histological, but ultrasonographic or
\ntomographic images may allow a non-invasive description of vacuolar hepatopathy. In
\nhuman medicine, unenhanced CT is considered the best way to assess fat accumulation
\nwithin the liver. The aim of the study was to describe an uncommon tomographic
\npattern of canine and feline vacuolar hepatopathy with fat accumulation.
\nMethods
\nOne Labrador retriever and one domestic shorthair cat were referred to our centers
\nafter hematology, biochemistry and abdominal ultrasound, for a total body CT due to a
\nsuspicion of hyperadrenocorticism syndrome and hepatic disease, respectively. Blood
\ntests revealed an increase of liver enzymes in both animals, albumin/globulin ratio in
\nthe cat and blood urea nitrogen, glucose and triglycerides in the dog. Ultrasonography
\nshowed an increased liver echogenicity and - in the dog - increase in size of the left
\nadrenal gland. Plain and post-contrast (600 mg/kg i.v. Iodine) total body CT acquisition
\nwith soft tissue algorithm was performed. Circular regions of interest (ROI) with an
\narea of 2 cm2 were drawn over the liver avoiding major hepatic and portal vessels. For
\neach ROI, mean (standard deviation, SD) attenuation values (HU, Hounsfield Units)
\nwere recorded. Mean (SD) hepatic attenuation values of the two investigated animals
\nwere compared to the mean (SD) hepatic attenuation values measured in 10 dogs and
\n10 cats which underwent CT for unrelated liver diseases, used as control. Fine needle
\naspiration (FNA) and tissue core biopsy of the liver were also performed in the cat and
\nin the dog, respectively.
\nResults
\nOn pre-contrast images, the liver of the dog had a mean (SD) hepatic attenuation
\nvalue of -18,39 HU (12,78) (Fig. 1), while the liver of the cat had attenuation of -25,04
\nHU (7,15) (Fig. 2). The average of the mean (SD) hepatic attenuation values in all
\ndogs without hepatic diseases was 63,85 HU (12,03), while it was 55,25 HU (6,77) in
\nthe cats with normal liver. Post-contrast CT images revealed a mean (SD) hepatic
\nattenuation value of 30,04 HU (15,7) in the dog and 25,61 (10,48) in the cat. The
\ncontrol animals presented an average of the mean (SD) hepatic attenuation values of
\n136,28 (15,02) in dogs and 129,25 (9,66) in cats. (Fig. 3). The tomographic diagnosis
\nof lipidosis in the cat and steatosis/hyperadrenocorticism in the dog were confirmed by
\ncytological and histopathological examination, respectively.
\nDiscussion/Conclusions
\nNegative hepatic attenuation values with fat storage describe a hypoattenuating
\nreverse CT pattern of the parenchyma, when compared to hepatic vascular structures.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.149
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.131
GPT teacher head0.331
Teacher spread0.200 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2016
Admission routes1
Has abstractyes

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