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Record W7116669012 · doi:10.9734/jsrr/2025/v31i123822

Clinico Diagnostic Aspects of Hepatogenic Ascites in Dogs

2025· article· en· W7116669012 on OpenAlexaboutno aff
Kasthuri Dhileep, Lakshmi Rani N, Sreenu M

Bibliographic record

VenueJournal of Scientific Research and Reports · 2025
Typearticle
Languageen
FieldMedicine
TopicLiver Disease and Transplantation
Canadian institutionsnot available
Fundersnot available
KeywordsAscitesVomitingAlbuminEdemaHypoalbuminemiaAbdominal FluidMelena

Abstract

fetched live from OpenAlex

The present investigation was carried out to study the occurrence and causes of ascites in dogs. Out of 4704 dogs screened during the study period 48 dogs were found positive for ascites. Among 48 ascitic dogs, 21 dogs had ascites due to hepatic origin (43.75%). Age wise occurrence was higher in dogs aged between 1-4 years. Labrador retriever was most commonly affected breed. Male animals were most commonly affected than females. The major clinical signs recorded were inappetence (66.67%), vomiting (47.61%), melena (42.85%), weight loss (42.85%), pedal edema (38.09%), dark yellowish urination (71.42%) and icterus (38.09%). Out of 21 dogs suffered with ascites of hepatic origin, grade 1 ascites was observed in 2 dogs, grade 2 ascites in 10 dogs and grade 3 ascites was observed in 9 dogs. Haemato biochemical analysis revealed a significant (P<0.05) decrease in haemoglobin, total protein and albumin whereas significant (P<0.05) increase in ALT, AST ALP levels were noticed in affected dogs. Ground glass appearance with loss of serosal details (75%), hepatomegaly (20%) were major abdominal radiographic findings. Sonographic examination revealed anechoic peritoneal fluid in all the dogs (100%). Overall, the findings emphasize that hepatogenic ascites remains a significant clinical condition in dogs and requires a comprehensive diagnostic approach. This includes incorporating haemato-biochemical analysis together with imaging evaluation for identification of hepatic disorders and to enable timely therapeutic intervention.

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)0.004
metaresearch head score (Gemma)0.001
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 score0.018
Threshold uncertainty score0.136

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.049
GPT teacher head0.399
Teacher spread0.350 · 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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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