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Record W4406936583 · doi:10.56557/ajoair/2025/v8i1506

Chronic Kidney Disease in a Dog: A Case Report

2025· article· en· W4406936583 on OpenAlexaboutno aff
Md Naim Uddin Nipu, Nazmul Hasan, Md Zakirul Alam Bhuiyan

Bibliographic record

VenueAsian Journal of Advances in Research · 2025
Typearticle
Languageen
FieldVeterinary
TopicVeterinary Medicine and Surgery
Canadian institutionsnot available
Fundersnot available
KeywordsKidney diseaseMedicineInternal medicine

Abstract

fetched live from OpenAlex

This report describes a 10-year-old Labrador Retriever dog who was found to have chronic kidney disease (CKD). Classic CKD signs, such as weight loss, increased thirst, decreased appetite and frequent urine were observed in the patient. Tests in the lab revealed proteinuria coupled with increased blood urea nitrogen and creatinine levels. The renal cortex displayed structural changes measuring 7.5 mm on ultrasound. Radiographic evaluation revealed an enlarged heart, with the vertebral heart score (VHS) elevated at 12.5v in the right lateral view, exceeding the reference range of 10.2–11.4v. Additionally, cardiac troponin-I levels were elevated, further supporting evidence of cardiac involvement. A comprehensive treatment plan was initiated. Medication to control blood pressure, supportive care for secondary problems, and a renal diet to reduce protein and phosphorus consumption. To evaluate the effectiveness of the treatment and make the required modifications, routine monitoring of kidney function and general health was essential. Two months later, all the biochemical parameters improved and the renal cortex returned to normal, measuring 6.3 mm. Additionally, cardiac troponin-I levels and heart size, as indicated by the VHS score, normalized to 10.5v, reflecting the positive response to long-term therapy for chronic kidney disease (CKD) and its associated cardiac complications. This case underscores the critical need for early diagnosis and appropriate treatment in dogs with CKD to enhance quality of life and extend lifespan.

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)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.362
Threshold uncertainty score0.486

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.125
GPT teacher head0.503
Teacher spread0.377 · 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 designCase report
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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